Review ArticleResource Allocation in Millimeter-WaveDevice-to-Device Networks

Filbert Onkundi Ombongi ,1 Heywood Ouma Absaloms,2 and Philip Langat Kibet3

1Pan African University Institute for Basic Sciences, Technology and Innovation, Juja, Kenya2Department of Electrical and Information Engineering, University of Nairobi, Nairobi, Kenya3Department of Telecommunication and Information Engineering, Jomo Kenyatta University of Agriculture and Technology,Juja, Kenya

Correspondence should be addressed to Filbert Onkundi Ombongi; onkundifilo12@gmail.com

Received 11 July 2019; Accepted 3 December 2019; Published 26 December 2019

Academic Editor: Marco Anisetti

Copyright © 2019 Filbert Onkundi Ombongi et al. )is is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in anymedium, provided the original work isproperly cited.

Recently, the mobile wireless communication has seen explosive growth in data traffic which might not be supported by thecurrent Fourth Generation (4G) networks.)e Fifth Generation (5G) networks will overcome this challenge by exploiting a higherspectrum available in millimeter-wave (mmwave) band to improve network throughput. )e integration of the millimeter-wavecommunication with device-to-device communication can be an enabling 5G scheme in providing bandwidth-intensiveproximity-based services such as video sharing, live streaming of data, and socially aware networking. Furthermore, the currentcellular network traffic can also be offloaded by the D2D user devices thereby reducing loading at Base Stations (BSs), which wouldthen increase the system capacity. However, the mmwave D2D communication is associated with numerous challenges, whichinclude signal blockages, user mobility, high-computational complexity resource allocation algorithms, and increase in interuserinterference for dense D2D user scenario. )e paper presents review of existing channel and power allocation approaches andmathematical resource optimization solution techniques. In addition, the paper discusses the challenges hindering the realizationof an effective allocation scheme in mmwave D2D communication and gives open research issues for further study.

1. Introduction

)e mmwave D2D communications is a 5G network en-abling technology to satisfy multigigabit data demands. )ehigh data rate links and the directional coverage of mmwavenetworks makes it suitable for the provision of proximityservices through D2D communications. )e directionalcharacteristics at the mmwave band minimizes the inter-ference between D2D pairs in dense D2D pair networkscenario [1].

However, mmwave communication has a challenge ofsignal blockage which results in more signal losses for theindoor network scenario. In addition, whenmobility of usersis allowed, then overall network performance is affected dueto the need of frequent hand overs from one cell to the otherand frequent updating of the base station on D2D user

location variations. )erefore, the mmwave D2D will berealized with some modifications on the system and hard-ware, such as scheduling algorithms and use of directionalantennas. )e main benefits of integrating mmwave andD2D communications are provision of bandwidth-intensiveapplications and services, offloading traffic from the currentcellular systems, higher throughput or system capacity,higher energy efficiency, and lower energy consumption[2, 3]. However, due to low antenna height compared to theBSs and narrow beamwidth, the mmwave D2D communi-cation is more vulnerable to signal blockages from buildings,trees, walls, and human bodies; this is the major challengefor successful operation of D2D communications in themmwave band.

)e challenges in mmwave D2D communication not-withstanding, the D2D communication in the mmwave

HindawiMobile Information SystemsVolume 2019, Article ID 5051360, 16 pageshttps://doi.org/10.1155/2019/5051360

band offers more benefits compared to its communication inthe sub-6GHz band. When devices in close proximitycommunicate directly it results in improved capacity andreduced latency in comparison to BS-enabled communi-cation. In addition, there is minimization of the load on thebackhaul network thereby increasing the network capacity.)e direct D2D communication is performed at a lowertransmit power which results in better energy efficiency. )eD2D technology can also improve coverage and throughputfor cell edge users who might be experiencing weaker re-ception of signals by acting as relays.

Most research studies that have been carried out for D2Dcommunications have majorly considered in-band D2Dcommunication. In in-band D2D communication, the D2Duser devices reuse spectrum resources of cellular user devicesbelow 6GHz [4, 5]. )e surveys that have been published onD2D communication have dwelt on in-band D2D com-munication, where there is complexity in modelling inter-ference originating from the D2D users to the cellular userequipment. In [6], a survey on D2D communication in-volved the concepts such as network discovery, interferencemanagement, provision of proximity services, and securityin the network. )e survey also proposed vehicle-to-vehicle(V2V) communication, mmwave spectrum, social-awareD2D networks, and simultaneous wireless information andpower transfer as areas that needed further study for in-bandD2D communication. In [7], a comprehensive analysis onin-band D2D challenges and open research issues werediscussed based on mode selection, device discovery, radioresource and interference management, mobility manage-ment, and privacy and security schemes which were notconsidered for in-band D2D surveys given in [8–12].

In [13], an overview of in-band D2D communicationresource allocation schemes was given by categorizing re-source optimization objectives, constraints, and associatedmathematical solutions. )ese studies have shown that thepractical D2D communication will be operated in themmwave band with a switch to the microwave bandwhenever there are nonline of sight conditions (NLOS). )emain goals of this survey are as follows:

(1) Present the issues related to mmwave D2D networkswith a comprehensive analysis of the resource al-location mechanisms with a view of proposingtechniques which can provide better results

(2) Describe the challenges in relation to resource allo-cation techniques in mmwave D2D communication

(3) Provide open research directions in the area ofmmwave D2D communication

)e survey is the first one in the area of mmwave D2Dcommunication which is sometimes referred to as outbandD2D communication in terms of resource allocation ap-proaches by looking at the resource optimization objectives,solution approaches, and performance parameters. In [14],an overview of the D2D use cases was given by categorizingD2D network based on commercial and public safety ser-vices. )e outband D2D considered in this survey is whereD2D users coexist with the Wi-Fi Direct as a dominant

unlicensed D2D communication network in the industrial,scientific, and medical (ISM) spectrum. In [15], a survey wasgiven by considering the routing problem in D2D com-munication. It also considered different routing techniquesapplicable to D2D communications in terms of their relaytype and spectrum used.

However, this review covered only the multihop routingprotocols which were proposed for mmwave D2D networksin [16–18]. )e resource allocation problem in mmwaveD2D networks was not considered in this review.

In [19], an overview was given on implementation of ahybrid D2D communication network covering the mmwaveband and the conventional cellular band. It also gave themmwave propagation features and technical issues for D2Dcommunication. However, the resource allocation problemand the associated algorithms were not considered in thisreview.

)e remainder of the paper is organized as follows.Section 2 discusses the applications of mmwave D2D com-munications, Section 3 gives resource allocation mechanisms,network scenarios, and performance targets, Section 4 dis-cusses the mathematical solution techniques, Section 5 looksat challenges in mmwave D2D communication, Section 6discusses the merits and demerits of the mathematical so-lution techniques, Section 7 deals with the future researchdirections, and Section 8 gives the conclusion.

2. Applications of mmwaveD2D Communication

)e device-to-device networks can be deployed in themmwave band to mitigate the complexity of interferencemodelling in the sub-6GHz band. )e mmwave D2D findsits application in different areas to offer proximity-basedservices such as public safety communications, offloadingtraffic from current cellular networks, wearable devices andInternet of )ings (IoTs), and social-based networking forcontent delivery services [20]. )ese applications have beendiscussed briefly and summarized in Figure 1.

2.1. Public SafetyCommunication. )eD2D communicationcan be used to enhance public safety in case of unexpecteddisasters such as fire outbreak, earthquake, and flooding byintegrating D2D to the conventional network infrastructure[22, 23]. )e D2D network can then be activated wheneveran emergency occurs to set up communication in a fasterway.

2.2. Traffic Offloading. Due to an increased number of de-vices which includes tablets, smartphones, wearable devices,and laptops, coupled with an increase in demand for theservices offered by them, has brought the challenge of thecurrent 4G network not meeting the required traffic capacityand quality of service. )erefore, the deployment of D2Dcommunication in 5G networks has shown that the currenttraffic can be offloaded to the D2D devices [24].

2 Mobile Information Systems

�is D2D-based tra�c o�oading can assist in mini-mizing end-to-end latency especially for delay tolerantapplications.

2.3. Wearable Devices and Internet of �ings (IoT). �e 5Gnetwork has a main goal of providing connectivity to dif-ferent types of devices. �e D2D devices can be deployed asrelays in buildings and in areas where there is no coverage ofthe sensor devices, and then the home appliances are allowedto have D2D communication mode thereby reducingcommunication through the Base Station (BS).

�e smart watches and medical monitoring devices aresome of the wearable devices that can save power since theycommunicate through D2D relays [25]. Since D2D com-munication is seen to be versatile, it can easily be applied inIoT and wearable devices for provision of low-power shortrange communications [26].

2.4. Social-Based D2D Networking. �e D2D devices canallow direct communication between devices that are closeto each other to deliver or share multimedia content. �iscan be made possible by applying proper device discoveryand pairing algorithms to enhance performance targets suchas energy e�ciency, sum rate, and connection density. Someof the services that can be o�ered as social-aware D2Dservices include local advertisements, multimedia contentsharing, and playing games [6, 27].

3. Resource Allocation Schemes in Millimeter-Wave D2D Networks

�is section gives an overview of the resource allocationapproaches that have been carried out in millimeter-waveD2D networks by considering the solution approaches,network scenarios, and performance targets. Table 1 gives a

comparison of some of the spectrum and power allocationapproaches that have been studied for mmwave D2D net-works. �e resource allocation solution approaches arecovered in detail in Section 4.

Chang and Teng [28] had an objective of maximizing theenergy e�ciency (EE) and reducing transmit power. �emillimeter-wave optimization problem was mathematicallyformulated as a nonlinear programming power allocationproblem, which was solved by an iterative algorithm byconsidering a full duplex relay. �is study proposed amodi�ed bottleneck e�ect elimination power algorithm tominimize transmit power and further improve energy ef-�ciency while maintaining end-to-end network throughput.�e iterative power allocation algorithm and bottlenecke�ect elimination power algorithm were combined by ap-plying the Gale–Shapley (GS) and Hungarian matchingalgorithms. �e simulated results showed that relay con-sumed power reduced by 45.9% and that of D2D pairsreduced by 61.6%. In addition, the EE was improved by32.3% when compared to conventional techniques appliedin [45].

Feng et al. [29] considered resource sharing mechanismandmode selection to optimize the sum logarithmic rate in asingle cell mmwave cellular network based on time divisionduplexing (TDD). In this network scenario, multihop D2Drelays or multibeam re�ections in a TDD mode served theblocked users. �e optimization problem was divided intotwo parts. One part was dealing with the resource-sharingsolution while the other was dealing with adaptive modeselection. �e resource-sharing solution was developed to amixed-integer programming (MIP) function which wasremodelled as a convex programming function. �e opti-mization function was solved by applying the Lagrangianmethod for optimal solution. �e simulated results showedsome performance gain when compared to the conventionalallocation techniques.

Standard UE-BScommunication

D2D-based vehiclecommunication

Macro BSPico BS

IOT in Healthcare Wearables

Trafficoffloading

D2D based multihoptransmission

Public safety service

Content sharing/local

multicasting

Social-based networks

Localadvertising

D2D linkTraditional UE-BS link

Figure 1: Application areas of D2D Communication [21].

Mobile Information Systems 3

Table 1: Summary of mmwave D2D communication resource allocation solutions.

Reference Target/objective Method Performance metric

[28]Maximize energy efficiency and reduce

transmission power in a full duplex (FD) relay-aided mmwave D2D communication

Lagrange dual decomposition andKarush–Kuhn–Tucker (KKT) conditions

Matching theory for relaying

Energy efficiency(EE)Transmit power

Jains’ fairness index

[29]

Maximize sum rate in a single-cell mmwavetime division duplex cellular network byconsidering joint adaptive selection for

multibeam reflection of NLOS devices andD2D relays

Lagrangian dual-based algorithm Sum rateJains’ fairness index

[30]

Maximization of throughput in an outdoormmwave small cell environment with a trade-off between number of admitted devices and

interference constraint.

Heuristic algorithm )roughputSatisfaction ratio

[31]

Joint relay selection and power allocation tomaximize system throughput and minimizeaggregate transmission power by taking data

rate threshold and total transmit powerconstraints

Matching theory Transmit power)roughput

[32]

Optimal subchannel allocation for underlay tomaximize sum rate and spectrum efficiency forD2D communication in outdoor mmwave

scenario

Iterative water filling algorithmSum rate

Spectrum efficiencyJains’ fairness index

[33]Optimal subchannel allocation for access andD2D links in a densely deployed multiplemmwave small cells to maximize sum rate

Coalition game Sum rate

[34]

Enhance system throughput and spectrumefficiency in an urban scenario in the E-bandby reducing interference from multiple D2D

pairs

Heuristic algorithm )roughputD2D efficiency ratio

[35]

Maximize the network sum rate in D2D-enabled communication in heterogeneous

cellular networks by combining mmwave andsub-6GHz

Coalition formation game Network sum rate

[36]

Maximize EE of the CUs served by eithermacrocells or mmwave small cells by

considering simultaneous subcarrier andpower allocation to satisfying a given QoS levelfor D2D pairs. )e macrocells operate at2.4 GHz and small cells operate at 28GHz.

Lagrangian and Hungarian methodEnergy efficiency

Outage probability of D2Dpairs

[37]

Energy efficiency maximization for cellularand D2D user devices. )e cellular devices areeither served by macrocells or mmwave smallcells and the QoS requirements of D2D users

are maintained.

Lagrange technique and KKT conditionsHungarian method

Energy efficiencySum rate

Outage probability

[38]

A Stackelberg game-based time-sharingtechnique proposed for interfering D2D

communication paths to maximize throughputat 60GHz

Stackelberg game )roughputD2D user density

[39]Transmit power minimization scheme by

considering device association and beamwidthselection in a 60GHz mmwave D2D network

Particle swarm optimization (PSO) Transmit powerAchievable rate

[40]

Resource allocation, beam selection, andinterference coordination in integrated

mmwave and sub-6GHz network scenariowith two-hop D2D relaying.

Graph theory )roughput

[41]Resource sharing in D2D communication for ammwave and 4G system architecture with

TDMA-based MAC structure

Nonlinear integer programmingHeuristic resource sharing scheme Network capacity

4 Mobile Information Systems

In [30], an interference mitigation and spectrum re-source allocation scheme was proposed in an outdoormmwave cell having uplink cellular and D2D communi-cation for maximization of the attained sum rate by trading-off between density of admitted user equipment and in-terference constraint. A mathematical optimization problemwas formulated as a nonlinear integer function which washard to find a solution. )is complexity was overcome byproposing a heuristic algorithm for uplink interference al-leviation so that the solution of the optimization functioncan be determined.

Ma et al. [31] proposed a relay-enabled mmwave D2Dcommunication to offer a solution of battery capacity limi-tation of the user devices with high-data rate requirement inemerging applications with an objective of maximizing sys-tem throughput andminimizing the total transmit power.)eoptimization function was formulated as a multiobjectivecombinatorial problem to address simultaneous selection ofrelay and power allocation. )e optimization function was tosatisfy the constraints of the D2D minimum data rate andaggregate transmission power and relay aggregate transmis-sion power. )e optimization problem of this nature caneither be solved by branch and bound, outer approximation,or Benders decomposition techniques.

)ese methods have the challenge of some degree ofcomplexity in determining solution to the polynomial. )iswas overcome by proposing matching-theory based solu-tions. )e weighted bipartite graph construction was ap-plied as a one-to-one matching game to provide solutionsthat can be easily tracked for such resource optimizationproblem. )e results of the proposed algorithms showedsome performance improvement compared to the aug-mented random search (ARS) and distributed relay se-lection algorithms.

In [32], a resource allocation for the outdoor mmwaveD2D underlay network was considered to maximize spec-trum efficiency by ensuring the resources are distributedfairly within a cell. )e sum rate maximization problem wasformulated to ensure optimal resource allocation. )is studywas carried out with an aim of allowing more than one userresource-sharing scheme without reducing the spectrumefficiency to attain an improved system throughput. Forcomplexity reduction, a threshold was imposed on the usersper resource block with traffic environment adaptation. )efrequency of operation was taken to be 28GHz with em-phasis on network-assisted D2D communication.)e powerallocation problem for the D2D users in the same cell was

solved by an iterative water filling algorithm by consideringinterference experienced by the resource blocks with sub-optimal results being obtained.

In [33], a millimeter-wave network with dense deploy-ment of small cells was considered to optimize D2D linkssubchannel allocation process. )e sum rate of accessing themmwave network and individual D2D link sum rate wasmaximized by formulating the channel assignment as acoalitional game. )e results showed that the proposedchannel assignment algorithms offered an improved per-formance compared to other channel assignmenttechniques.

In [34], an E-band D2D undelay millimeter-wave net-work was considered by proposing an algorithm to optimizethe resource allocation process. )e study considered anurban area with an aim of improving spectrum efficiencyand throughput. In addition, the proposed algorithm was tominimize interference in a dense network, where the D2Dpairs reuse same cellular users’ resource blocks. )e for-mulated optimization function was solved by a heuristicalgorithm in an outdoor realistic path loss model at 73GHz.)e proposed algorithm offered better overall systemthroughput and reduced interference.

In [35], an uplink resource allocation optimizationfunction was mathematically expressed as a nonlinear op-timization function for microwave and mmwave band users,where there were multiple D2D pairs. )e sum rate wasmaximized by applying a coalition formation game. )eproposed scheme had a rapid convergence rate and reachedNash equilibrium with suboptimal solution obtained. )eproposed game offered improved results than other practicaltechniques.

In [36], a hybrid D2D-enabled cellular network wasdeveloped that operates on the microwave and millimeter-wave band. )e macrocells had a number of millimetersmall cells within their coverage area. A downlink trans-mission was considered by proposing a resource-sharingscheme to maximize the energy efficiency of opportu-nistically served cellular users by macrocells or millimeter-wave small cells with QoS threshold level for the D2Dpair’s guaranteed.)e power control scheme for D2D pairswas formulated that was self-adaptive while maintainingminimum QoS level and cellular users’ interferencethreshold. )e problem was decomposed into D2D pairs/CUs’ power allocation and subcarrier allocation for themicrowave BS associated cellular users. )e problem ofallocating power to D2D and cellular users was solved by

Table 1: Continued.

Reference Target/objective Method Performance metric

[42] Maximization of mmwave D2D throughput byjoint allocation of transmit angle and time slot Graph theory )roughput

[43] Minimize D2D link transmit power andmaximize the achievable throughput Stackelberg game Transmit power

)roughput

[44]

Optimize outage probability for uplink cellularand D2D communicating users for a D2D-enabled mmwave network with clustered D2D

pairs

Stochastic geometry and Laplace transform Outage probability

Mobile Information Systems 5

an algorithm based on Lagrangian multiplier techniquewhile the subcarrier allocation was solved by the Hun-garian algorithm. )e results were suboptimal and showedsome improvement in EE maximization compared to thealgorithms for sum rate maximization and consumedpower minimization. A similar study, in [37], for EEmaximization with a trade-off between energy efficiency,outage probability, sum rate at different QoS levels andvariation of D2D pairs, and cellular users’ density wascarried out. )e energy-aware radio resource managementscheme was formulated for optimization of the achievablerate and energy efficiency of cellular users with minimalQoS requirement and maximum input power constraint.)e optimization problems were reformulated to convexsubproblems by using the Lagrange multipliers and dualdecomposition techniques. )en, the optimal power al-location for a microwave BS and mmwave BS was de-termined by using the Karush–Kuhn–Tucker conditions.)e multilevel water filling algorithm was then applied todetermine optimal power allocation between the micro-wave BS and associated users. )e water level depended onthe beamwidth for a transmitter and receiver. )e Hun-garian algorithm was applied to solve the subcarrier al-location problem for suboptimal allocation strategy.

Li et al. [38] developed a time-based resource allocationmechanism for interfering D2D communication links tomaximize throughput. )e resource optimization problemwas modelled as a Stackelberg game with interferencecausing links paying a higher price compared to those oneswhich are not causing interference. )e interference causingD2D links were scheduled to access mmwave networkconcurrent transmission. )e optimization function wasalso formulated as a noncooperative Nash game tomaximizethe D2D rate and at the same time obtain a transmissionpower control that is distributed between interferencecausing links. In addition, there was a modification of thepricing strategy by setting an interference limit to guaranteethe transmission quality at 60GHz carrier frequency. )eresults showed an improvement in the network throughputcompared to an algorithm based on vertex colouring pro-posed in [46].

Zhang et al. [39] proposed a transmit power optimi-zation scheme for a millimeter-wave D2D network by in-tegrating device association and beamwidth selection. )eGaussian directional antennamodel’s roll-off feature and thereflective nature of the two-ray channel model were bothintegrated in the network scenario. )e formulated opti-mization problem to minimize the transmission and energysaving in mmwave networks was a nonlinear problem whichis complex in determining its solution. )is problem wassplit into two with one dealing with device association andthe other for beamwidth selection. )e device associationproblem was solved by a distributed framework whileparticle swarm optimization (PSO) algorithm was used tooptimize beamwidth scheme. )e result showed some im-provement by reducing transmission power, mitigatinginterference, and improving the sum rate of the systemcompared to other interference mitigation schemes whichdo not consider device transmit power.

A hybrid network scenario consisting of two-hopdownlink D2D relaying was proposed in [40] by integratingthe mmwave and the sub-6GHz band as a way of avoidingblockage and coverage extension for an outdoor urbannetwork scenarios with different densities of site deploy-ment. )e resource allocation mechanism over two sub-carriers was integrated with relay and beam selection toimprove data rate for users at the cell edge thereby ensuringconsistent user experience. )e sub-6GHz band was re-sponsible for the network control and communication re-liability while the mmwave communication provided highthroughput improvement. )e multiobjective problem wasformulated and solved by graph theory that had the capa-bility of selecting a relay, allocating resources, and coordi-nating interference. )e graph colouring method addressedallocation of channel and coordination of interferenceproblems by modelling interaction between neighbouringlinks as an interference graph which ensured proper se-lection of the beam and relay, allocation of resources, andcoordination of interference. )e results showed a highersystem throughput with some consistent user experience.However, a substantial number of users missed mmwavetwo-hop connectivity when the intersite distance was above400m. Another challenge was difficulty in determining anappropriate incentive for user equipment (UE) that can beused as relays.

In [42], a dense heterogeneous D2D network wasstudied by allocating both the transmit angle and time slotfor a D2D transmitter in a dense network. )is involvedmodelling interference graphs to show the status of in-terference in the system at any given time. )e transmitangle and time slot allocation optimization problem wassolved by graph theory-based iterative algorithm. )e re-sults showed a reduction in simulation complexity andimproved performance compared to exhaustive searchmethods. However, the obtained results were suboptimaland the algorithm is not efficient for a higher or smallernumber of D2D devices. )is is due to minimal adjustmentof the transmit angle at such cases since most D2D linkshave direct communication.

In [43], an interference control scheme supporting fullfrequency reuse was developed based on the Stackelberggame in a mmwave D2D network. )e D2D pairs weresharing the uplink resource blocks with the mmwave smallcells. )e resource allocation optimization function wasaimed at minimizing interference and transmit power whilemaximizing the achieved D2D throughput. )e resultsshowed a faster convergence rate and an improved per-formance compared to maximum sum rate assignment al-gorithm used in [34].

A single tier uplink mmwave D2D-enabled cellularnetwork was developed to analyse the outage probabilityperformance for the cellular and clustered D2D users in [44].)e study formulated a mode selection and spectrum-sharing scheme to optimize the outage probability ofmmwave cellular and D2D links. )e results indicated that ahigher outage probability was obtained when the network isdensified with cellular and D2D users or when there isconcurrent transmission for majority of the D2D user pairs.

6 Mobile Information Systems

3.1. Network Scenarios. )is gives a description on how thenetwork modelling was carried out. )is entails number ofcellular users, number of D2D pairs, single cell or multicell,either uplink or downlink communication or both, numberof BSs, and dimensions of the simulation area (which can beeither circular, hexagonal, or square). In [28], the networkmodel consisted of a single BS placed at the center of the cell,multiple D2D pairs, and multiple full duplex relays that wereuniformly distributed within the coverage area of the cell.)e antenna pair had an Orthogonal Frequency-DivisionMultiple Access (OFDMA) subchannel with a decode-and-forward relaying procedure. )e single-cell radius was takenas 300m with the distance between two ends of the D2Dtransmitter and receiver varying from 50m to 200m.

In [29], a single cell with a BS at the center servingmultiple D2D users which acted as relays for data transfer wasconsidered. )e network was assumed to operate in TimeDivision Duplex (TDD) mode for effective channel estima-tion.)e cell coverage was taken as 50m. In [30], the networkmodel hadmultiple D2D user pairs andmultiple cellular usersfor single cell having 500m radius. )e minimum rangebetween a cellular user and a D2D user that share resourceswas taken as 35m in an outdoor environment. )e samenetwork scenario was used in [34]. In [31], the network modelconsidered full duplex multiple relays supporting multipleD2D users for coverage expansion. )e study assumed thatthe BS, multiple relays, and D2D user pairs were controlled bythe same network operator. For every relay, there were twosets of antennas for full duplex communication throughdecode-and-forward procedure. )e D2D user pairs wereallocated orthogonal nonoverlapping channels and a dedi-cated band. )e study considered a single cell of radius 500mwith sectored directional antenna model for the transmitters.

In [32], a single hexagonal cell of radius 500m with a BSlocated at the center was considered for multiple cellular andmultiple D2D pair network scenario. )e D2D pairs werereusing the cellular user resources whenever the channelstate information (CSI) was favourable. )e maximum al-lowable range between D2D devices was taken as 20m. )ecell was subdivided into three sectors with each sector having100 resource blocks.

In [33], the study considered a network having multiplemmwave small cells with a dense implementation. Eachsmall cell had its own BS serving a number of users.)eD2Dcommunication was within a small cell and between smallcells. )e multiple D2D pairs shared the radio resources ofthe cellular users in a circular cell of radius 100m. )emaximum range between the D2D devices was taken to be5m and the IEEE 802.15.3c antenna model was used for thesimulated network scenario.

In [35], a heterogeneous single-cell network was con-sidered which supported communication in the microwaveand mmwave band. )e D2D user pairs were communi-cating in a dedicated mmwave band but were also allowed toreuse the microwave band if the link interference level isbeyond the set threshold. )e network had multiple D2Dpairs and multiple cellular users with the D2D pairs sharingthe uplink resources of the cellular users. )e distance be-tween the D2D user devices was taken as 10

�2

√m. A square

simulation area was considered with each side being 500mand the BS located at the center.

In [36], a hybrid heterogeneous cellular network wasconsidered covering the mmwave and microwave band withthe dual-band BS located at the center. )e multiple cellularusers and multiple D2D pairs were randomly distributedwithin the cell coverage. )e network had a number of smallcells within the coverage of the macrocell. )e macrocell hada radius of 400m, whereas the one of the small cell was 50m.)is network model was also used in [37] but with somemodifications such as defining some multiple virtual realityusers from the set of cellular users. In [38], a mmwave picocell with a piconet controller at the center was considered.)e network had multiple wireless user devices which wereassumed to communicate in half-duplex.

)e communication between different user devices wasimplemented by applying the super-frame structure con-sisting of a beacon, data transmission, and contention access.In addition, the network model had randomly distributedmultiple D2D pairs that were assumed to interfere with oneanother.)e ideal flat-top antenna array model was used as adirectional antenna for all the user devices. )e radius of thecell was taken as 50m and the distance between D2D devicesvaried from 5–15m.

It can be seen from this analysis, there is need to im-plement a dual band hybrid network that incorporates cellularusers, D2D pairs, full duplex relays, small cells, andmacrocellsin an outdoor and indoor environment. )e impact of anincrease in density of D2D pairs, cellular users, relays, andsmall cells on the network performance should be studied indetail in terms of energy efficiency and achievable capacity.However, most of these studies have considered single-cellnetwork scenario.)erefore, there is need to extend this studyto a multicell network scenario and consider all the inter-ferences present in this kind of network set up. Table 2 showsthe comparison of performance evaluation based on thenetwork scenario, direction of communication, evaluationmethod, and solution techniques and tools.

3.2. Performance Targets. )ese are the metrics which aresupposed to be optimized in a mmwave D2D communi-cation network as shown in Table 2.

)ese performance targets have been considered in mostmmwave D2D communication networks and can be de-scribed as follows.

(1) Energy Efficiency (EE). )is is the total number of bitstransmitted per joule of energy consumed by the signalstransmitted and the transmitter and receiver circuitry [47].)e 5G network implementation should achieve an energyefficiency of at least 100 times the energy efficiency of thecurrent cellular networks [48]. )e higher the value of thismetric the better the performance which in turn results in anextended battery lifetime and energy saving for the given 5Gwireless network.

(2) .roughput. )is is defined as the maximum data rateachievable by summing the rate of all the links available for

Mobile Information Systems 7

all the D2D user pairs or cellular users. It is given inmegabits per second (Mbps). It can also be referred to asthe sum rate, achievable rate, or the capacity of a network.

)is is a significant metric for 5G networks as it is pro-jected to have a minimum peak data rate of 20 Gbps and aminimum user data rate of 100Mps to support bandwidth-

Table 2: Comparison based on performance metrics, network scenario, and solution method and tools.

Performance metric Reference Network scenario Solution method and tools Direction ofcommunication

Energy efficiency

[28] Single cell, multiple relays, multipleD2D pairs

Lagrange multiplers, Gale–shapleyand Hungarian-based simulation Uplink

[36, 37]

Dual band (microwave andmmwave), heterogeneous multicellmultiple cellular users, multiple

D2D users

Nonlinear optimization, weighted-Tchebycheff technique andmatching-theory simulation

Uplink

Transmit power

[28] Single cell, sectored antennamultiple relays, multiple D2D pairs

Shannon capacity model, Lagrangemultipliers, Gale–shapley andHungarian-based simulation

Uplink

[31]Single cell, sectored antenna, fullduplex multiple relays, multiple

D2D relays, three-sectored antenna

Shannon capacity relation, bipartitegraph construction,

Hungarian algorithm simulationUplink

[39] Single cell, clustered into differentclasses, multiple D2D users

Shannon capacity, particle swarmoptimization simulation in

MATLABUplink

[43] Single cell, multiple D2D users,multiple cellular users

Shannon capacity relation,stackelberg game simulation Uplink

Jain’s fairness Index

[28] Multiple relays, multiple D2D pairsLagrangian multipliers,

Gale–shapley and Hungarian-basedsimulation

Uplink

[29]

Single cell, sectored antenna,multihop D2D users, multiplecellular users operating in TDD

mode

Dijkstra’s algorithm and Lagrangiandecomposition Downlink

[32]Single cell, sectored antenna,multiple D2D users, multiple

cellular users

Rician fade modelling, Monte Carlosimulation Uplink

User throughput or sum rate orachievable rate

[29]Single cell, multiple D2D users,

multiple cellular users operating inTDD mode

Dijkstra’s algorithm and Lagrangianmultipliers Downlink

[30] Single cell, multiple D2D users,multiple cellular users Heuristic MATLAB simulation Uplink

[31] Full duplex multiple relays, multipleD2D relays, three-sectored antenna

Bipartite graph constructionHungarian algorithm simulation Uplink

[32]Single cell, sectored antenna,multiple D2D users, multiple

cellular users

Rician fading modelling and MonteCarlo simulation Uplink

[33]Multicell, IEEE 802.15.3c antennamodel, multiple D2D pairs, multiple

cellular users

Coalition formation gamemodelling Uplink

[34] Single cell, multiple D2D pairs,multiple cellular users Heuristic simulation Uplink

[35]Hetnet single cell, dual band,multiple D2D pairs, multiple

cellular usersCoalition game modelling Uplink

Network capacity [35]Hetnet single cell, dual band,multiple D2D pairs, multiple

cellular users

Shannon capacity relation andcoalition game modelling Uplink

Outage probability[36, 37]

Dual band (microwave andmmwave), heterogeneous multicell,multiple cellular users, multiple

D2D users

Nonlinear optimization andmatching theory simulation Uplink

[44] Multiple clustered D2D users,multiple cellular users

SINR modelling for cellular andD2D users Uplink

8 Mobile Information Systems

intensive applications in mmwave D2D communications[47].

(3) Connection Density.)is is the number of devices that areconnected per unit area. Since 5G networks will be char-acterized by densified number of users (cellular users andD2D users) and small cells in an outdoor and indoor en-vironment, it makes connection density to be an importantmetric in the implementation of mmwave D2D commu-nication networks.

4. Mathematical Optimization Techniques

)ere are different mathematical solution approaches thathave been applied in resource allocation problems inwireless networks with varying levels of complexity andconvergence to a solution.)emathematical techniques thathave been used in allocating resources for sum rate, networkthroughput, and energy efficiency maximization, minimizeinterference, energy consumed, and delay or latency formmwave D2D communication are discussed in the fol-lowing sections.

4.1.NonlinearOptimizationTechniques. )e optimization ofenergy efficiency inmmwave D2D communication networksfalls in a class of optimization problems referred to asfractional programs.)e objective function for the fractionalprogramming problems is a ratio of two real valued func-tions. If the numerator and denominator are differentiable,then energy efficiency function is pseudoconcave, and itssolution can be found by applying nonlinear optimizationtechniques such as Lagrangian technique or KKTconditions,iterative water filling, and bisection method. )e nonlinearsolution techniques do not guarantee convergence to op-timal point and they tend to have long computation timesi.e., slow convergence. )e Lagrangian method which isbased on KKTconditions is widely applied to solve problemsassociated with allocation of resources in mobile commu-nication networks.

)e Lagrangian technique has been applied to determinesolutions in mmwave D2D resource optimization problemsfor energy efficiency [28, 36, 37], sum rate [29, 37], Jain’sfairness index [28, 29], minimization of transmit power [28],outage probability of D2D users [36, 37], and network ca-pacity [41]. However, the computational complexity of thenonlinear optimization techniques will increase due tonetwork densification and mobility which make them un-suitable in future wireless networks which will be charac-terized by dense users and dense small cells.

4.2.Game.eory. A game can be defined as a mathematicaltechnique which can be used for modelling and analysis ofdifferent interactions amongst multiple players. Wirelesscommunication networks have employed game theory inmodelling the network interactions as a game. In thesewireless networks, the nodes or mobile devices being thedecision makers are the players which can compete orcooperate to maximize their payoff. )e main objective for

game formulation is the interdependence between thestrategies of the network nodes or devices in terms ofcomputational, storage, and spectrum resources in thepresence of interference in a wireless network. )e gamehas the capability of modelling and analyzing problemsassociated with resource allocation in D2D-enabled mil-limeter-wave communication networks.

)e games can be categorized into noncooperative orcooperative games. In noncooperative game, the strategicdecisions are made from interactions of competing playerswho choose their strategies or actions independently toenhance its own utility or reduce interference or losses(cost). )e noncooperative game has been applied in D2Dunderlay cellular communication for controlling power [49],spectrum sharing [50], user association [51], protocol de-sign, and resource allocation [52]. )e cooperative gamestudies the behaviour of rational players which can form acoalition or enforce a cooperative behaviour. )e playersform cooperative groups or clusters in this type of game.

)is game has emerged as a promising tool for D2Dnetworks to enhance performance. Despite the benefits ofcooperative or coalition game there exists a challenge ofadequate modelling, complexity, and user fairness fordensifiedmmwave D2D networks.)e cooperative game hasbeen applied in D2D underlay networks for security [53],relay selection and resource allocation [54], power control[55–57], interference management [58], and joint allocationof network resources and interference management [59].

Since there is cellular resource reuse by the D2D users,there has been a challenge of accurate interference modellingin underlay network systems. )is has been solved by uti-lizing the mmwave band to implement D2D networks forbandwidth-intensive applications to mitigate interferencecaused by multiple D2D users in underlaid communicationnetworks. In mmwave D2D communication, resource al-location and interference management problems have beensolved by cooperative game theory. For example, in mmwaveD2D networks game theory has been used to solve opti-mization problems for sum rate maximization [33, 35],network throughput, and D2D user density maximization[38].

However, these game-theoretic resource allocation ap-proaches in mmwave D2D studies have not consideredmulticell network scenario, where there is need to accountfor intercell interference, interference betweenmultiple D2Duser pairs, and D2D user and mmwave small-cell basestation interference. )e multicell network scenario is veryuseful for future networks as it will reduce energy con-sumption for cell edge users.

4.3. Matching .eory. )is is a branch of mathematics thathas been applied in wireless networks to analyse the per-formance based on mutual and dynamic relations amongdifferent kinds of rational and selfish users [60]. It has beenapplied in wireless networks to develop low complexity andhigh performance decentralized protocols [61]. It has alsobeen applied for resource allocation optimization in mi-crowave and millimeter-wave networks.

Mobile Information Systems 9

In [31], matching theory was applied to solve a relayselection problem for D2D communications in the mmwaveband to maximize system throughput and minimize totaltransmit power. A multiobjective combinatorial optimiza-tion function was formulated to perform joint power allo-cation and relay selection while satisfying minimal D2D datarate, aggregate D2D devices’, and relays’ transmission powerconstraints. Due to the complexity of Benders decomposi-tion, outer approximation, and branch and bound tech-niques, a one-to-one matching with weighted bipartite graphconstruction was applied to provide tractable solutions forthe joint relay and power allocation problem. )e resultsshowed some level of improvement compared to the aug-mented random search (ARS) and distributed relay selectionalgorithms. However, the centralized resource allocationscheme proposed in this study will not work well wherecooperation, self-organization, decentralization, and au-tonomous networks functions are required. In addition, thestudy did not consider energy efficiency and energy con-sumption metrics as a way of solving the challenge of batterylifetime for future wireless devices.

However, matching-theory application to resource al-location problems in wireless networks requires develop-ment of network scenarios that properly handle the intrinsicproperties which may include interference and delay.Moreover, if there is unstable matching, it can result in a BSswapping, its least preferred mobile user, with another asboth resource and user benefit from the swap which leads tounstable network operation.

4.4. Graph.eory. Graph theory is a mathematical tool thatcan be applied in modelling and analysis of interactions andrelationships in wireless networks. It has been applied inwireless networks to formulate algorithms for power control,interference management, and congestion control with low-computational complexity. In addition, graph theory can beapplied to model the interference relationships as interfer-ence-aware graphs are applied as interference-aware re-source allocation algorithm to reduce interference betweenD2D user devices.

)e graph theory techniques that have been applied inwireless networks include graph colouring and graph par-tition. )e graph colouring has been applied in D2D net-works to perform interference management, channelassignment, and resource allocation. )e graph partition hasbeen applied to perform clustering of network nodes ormobile devices. )e graph colouring technique can deter-mine solutions for a wide range of practical wireless networkoptimization problems.

Graph theory has been applied for downlink resourceallocation in the D2D underlaying communication network[62]. )e proposed resource allocation algorithm that wasbased on interference-aware graph gave near optimal resultson channel assignment solutions.)is helped the BS to obtainlocal awareness for each communication and interference linkin terms of channel gains. However, an interference graphbecomes complicated when the number of theD2D users goesup as envisioned in the future 5G wireless networks.

)e graph theory has also been applied for mmwaveD2D communication in [40] with a challenge of two-hopconnectivity for some users when the intersite distance isabove 400m.)erefore, for maximizing the benefits of graphtheory, algorithms need to be developed that are based onnew clustering mechanisms. )is will involve by combiningmode selection, QoS requirement, and energy efficiency inthe developed resource allocation algorithms.

4.5. Heuristic and Metaheuristic Techniques. )e evolutionsthat have been experienced in the field of computer sciencehave given rise to key technologies and solution complexityincrease for wireless communication resource allocationoptimization problems which has resulted in the need forspecialized software design techniques for large-scale ordense network optimization functions. )e mathematicalmethods such as dynamic programming, Lagrange opti-mization, heuristic techniques, and metaheuristic ap-proaches, such as PSO and genetic algorithm, have beenused for interference management and resource allocationalgorithms in wireless communication. )e GA and PSOalgorithms have the advantage of avoiding prematureconvergence to a local optima due to their stochastic searchmechanism. In addition, they are suitable for large-scalenetwork problems, where there are multiobjective optimi-zation problems with conflicting constraints.

)e GA has been applied for D2D communicationunderlaying cellular communication in [63] to maximizespectrum efficiency and minimize the interference. How-ever, this study considered single-cell network scenario withan assumption of no intercell interference, which however isa possible network scenario in practical D2D wirelesscommunication networks.

)e PSO algorithm was applied in [39] for transmissionpower minimization and achievable data rate maximizationby considering device association and beamwidth selectionin a 60GHz mmwave D2D network. )ese studies can beextended to other performance indicators such as energyefficiency and energy consumption to enhance batterylifetime for future mobile devices.

5. Challenges in mmwave Device-To-Device Communication

)is section deals with the issues that may hinder thedeployed mmwave D2D communication networks frommeeting their performance targets. )is involves themathematical solution algorithms applied, modelling of thechannel, and scaling up the network (densification).)ey arediscussed in detail as follows.

5.1. Mathematical Techniques. )ere are many algorithmsthat have been considered in the literature which are fastbut heuristic in nature which implies that the results ob-tained can be far from the optimal solution. )erefore, theapplication of the existing algorithms may not be able tomeet the strict performance requirements for fifth gener-ation (5G) D2D networks. )e numerical optimization has

10 Mobile Information Systems

been used to develop algorithms that are used to findsolution in D2D networks giving optimal or suboptimalsolutions with the assumption of no mobility for the mobiledevices. )erefore, considering the mmwave D2D networkscenario with some mobility will lead to more complexoptimization functions which possess high complexity. )ecurrently used mathematical tools may not work well in apractical dynamic mmwave D2D network due to longconvergence time.

Furthermore, the algorithms that have been applied sofar rely on some ideal assumptions to realize a mathematicaloptimization problem so that the problem can be tractable.However, these assumptions cannot work in a practical D2Dnetwork environment. In addition, in densified mmwaveD2D network, where there are multiple D2D devices,multiple mmwave pico cells, and mmwave femto cells un-derlaying a microwave macrocell network managementalgorithms, can be complex due to computational com-plexity, increased overhead at the base station in centralizednetwork management, and increased cost of gathering therequired information.

5.2. Channel Modelling. Most of the studies in the literaturehave considered a single cell with all communication linksmodelled as a single path loss parameter with inadequateprecision. )ere is no precise independence between thepath loss exponents and the link distances with the singlepath model. )us, there is need to determine accurate pathloss models that incorporates densification of mmwave smallcells. )e densification of these cells leads to some irregu-larities in the cell patterns and blockage in mmwave com-munications which brings in interference compositionwhich cannot be modelled as a single path loss exponent.

5.3. Network Densification. Network densification hasemerged as a key enabling 5G technology solution for ex-ponential growth of traffic demand. )e implementation offuture 5G networks will see the deployment of low-cost andsmall cells which are low-power operating in millimeter-wave spectrum especially in areas with higher traffic de-mand. )is will involve having dense number of small cellsand dense number of users supported by each cell. Since thenumber of small cells goes up, they will come in closeproximity to the D2D users operating within their coveragearea which reduces the intersite distance. )is will generatesevere intercell interference among neighbouring cells andbetween cells and users if they are operating in the samemmwave band. )is interference will reduce the capacity ofultradensified networks. Interference cancellation and in-terference coordination schemes can be effective solutionsfor curbing this interference. However, since the interfer-ence signals come from nodes and devices which are geo-metrically in close proximity with the intended receivers, theinterference becomes less divergent and spatially correlated.)is reduces the effectiveness of interference cancellationand coordination schemes in mitigating interference. Inaddition, the ultradense networks operating in the mmwaveband employ directional antennas that produce beams

which can be used to mitigate interference between theneighbouring nodes or devices. However, these directionalantennas rely on information about the physical locations ofthe transmitting nodes or devices and their correspondingreceivers. )ese nodes or devices change their positionsdynamically due to user mobility which in turn increases thenetwork overhead. )erefore, there is need to considerbeamwidth selection and beam alignment techniques jointlytogether with the resource allocation or scheduling tech-niques for energy-efficient D2D communication in ultra-dense networks where both are utilizing the mmwave band.)e backhauling in a network with dense millimeter-wavesmall cells can be a challenging issue for developing energy-efficient resource allocation schemes with backhaulingconstraint [64].

6. Merits and Demerits of theMathematical Techniques

)e mmwave D2D resource optimization techniques arecompared in Table 3 by giving the merits and demerits ofeach technique based on the computational complexity andthe ease of converging to a solution.

7. Future Research Directions

7.1. Machine Learning. )e mmwave D2D communicationnetworks will be characterized with much variations in thechannel conditions and network parameters. )e D2Ddevices and the base stations should have knowledge ofthese variations so that they can take appropriate action.)e interference between the D2D devices, interferencebetween femto cells and D2D devices, and interferencebetween femto cells all working in the mmwave band needto be accurately modelled for improved system perfor-mance in terms of throughput, energy efficiency, and la-tency. )erefore, machine learning tools can be applied tomodel this complex interference and radio resource allo-cation optimization problems without explicit program-ming. Machine learning can overcome some of thechallenges that have been noted in the existing algorithms.)e existing resource allocation algorithms cannot processor apply some data to solve optimization problems of whichsome meaningful information or patterns embedded in thedata might be lost. )is makes them difficult to adapt to thechanging network environment resulting in a systemperformance which deviates much from the optimal re-sults. In addition, most of the current algorithms are de-veloped from the numerical optimization tools whichresults in either optimal or near optimal solutions for theconsidered inband or outband D2D network scenarios in astatic environment. )ese developed algorithms are alsoassociated with high complexity and are based on someideal assumptions to make the formulated mathematicalproblem tractable. )is assumptions might not hold for thepractical mmwave D2D wireless networks. With all thesechallenges, machine learning enables the development ofcomputationally intelligent techniques with less complexityand overhead [65].

Mobile Information Systems 11

Some studies on inband D2D communication networksin sub-6GHz band have applied machine learning tech-niques. In [66], a cooperative reinforcement learning al-gorithm was developed to perform adaptive powerallocation in D2D communication to maximize D2D andcellular users’ throughput by maintaining a proper inter-ference level. In [67], joint power adaptation and mode

selection strategy were developed based on multiagentQ-learning algorithm was considered. )e study considereda realistic scenario where the base station had incompletechannel knowledge with the D2D users and cellular usershaving only private SINR information. )e near optimalresults were obtained and they showed an improved per-formance. However, this study did not consider link gains

Table 3: Merits and demerits of the mathematical solution techniques.

Mathematical technique Merits Demerits

Coalitional game

Cooperation in this game can offer betternetwork performance

)e users are capable of making contractswhich are mutually beneficial

Increase in complexity in large-scalecommunication networks

Stackelberg game Utility maximization for leaders and bestresponses for the followers are guaranteed

Need accurate channel state informationbetween the leaders and the followers

One-to-one matching

Can be used to characterize interactionsbetween heterogeneous network nodes or

devices with different objectives andinformation

Has capability of defining user preferences ina heterogeneous network and UEs QoS in

wireless networks)e match theoretic algorithms’ solutions

converge to a stable stateMatch theoretic algorithms can beimplemented efficiently with a self-

organizing feature

It provides multiple stable points which needproper selection of appropriate matchingOptimality of a stable solution cannot be

guaranteedDynamic algorithms require additionalsignaling for exchange of proposals in

wireless networks.

Lagrangian dual decomposition andKarush–Kuhn–Tucker conditions

Gradient-based nonlinear optimizationtechniques have relatively low-computational and set up time

High-dimensional and multimodalproblems require infinite running timeGlobal optimality is not guaranteed)e continuity and differentiability

assumption for the objective function doesnot hold for practical network systems

Iterative water filling

Operations performed by this algorithmincludes only basic arithmetic in addition to

the logarithm function which can beimplemented as a look up table

Increased complexity for multicell,multiuser, and multiantenna networks

Genetic algorithm

Random mutation offers a wide range ofsolutions

It has a large and wide solution searchingspace capability

Has potential of solving multiobjectiveoptimization problems

Use of the fitness function for evaluationoffers the capability of extending tocontinuous and discrete optimization

problems

Difficult to develop good heuristic whichreflects what the algorithm has carried outDifficult to choose parameters such as

number of generations and population sizeExtremely difficult to fine tune to get

enhancement in performance

Particle swarm optimization

It has guidelines for selecting theoptimization parameters

Has variants for real, integer, and binarydomains

Provides best solutions due to the capabilityof escaping from the local optima

Converges rapidly

Weak local search abilityIt has premature convergence

Graph colouring

Has suitable tools for modelling andanalyzing wireless networks

Low-computational complexity for D2Dnetworks

Provides a common formalism for differentwireless network problems

Difficult in modelling the user interactionsfor densified (large scale) networks

12 Mobile Information Systems

between eNB and D2D pair, between D2D pairs in theformulation of the reward function, which are very useful inthe determination of an improved transmission power, andSINR level, and for throughput maximization.

In [68], a power control scheme for D2D users wasconsidered by employing a distributed Q-learning algorithmincorporating a distributed and collaborative update of theQ-values. )e resource allocation scheme was maximizingthroughput of D2D pairs and cellular user in a single-cellnetwork. )e proposed algorithm performed better that therandom resource allocation. However, the study did notconsider the channel gains associated with the corre-sponding reward function.

In [69], a transmit power control scheme based on deeplearning was considered for weighted D2D sum rate max-imization. )e D2D users were sharing radio resources withthe cellular users in the uplink communication with oneresource block considered in the system model. )e findingsshowed that the proposed approach obtained high-weightedsum rate with a lower computation time compared with thenumerical iterative optimization methods.

However, all these studies which have been performedon microwave D2D underlaid networks can be extendedto mmwave D2D communication in mmwave pico cells tomitigate the interference. )e interference between theD2D users and interference between the D2D users andthe mmwave pico cells should then be taken into accountfor energy efficiency and energy consumption maximi-zation which are the key performance indicators in future5G networks to prolong battery life. In addition, theresource allocation based on reinforcement learning canbe extended for a multicell network scenario in themmwave band where every cell can be modelled as anagent.

7.2. Channel Modelling. )e mmwave D2D communicationwill require some additional analysis and a clear under-standing of D2D links in the mmwave band. )is will alsoinvolve the experimental verification of beam switchingmethods which includes the practical implementation of theantenna arrays. )e improvement of the mmwave D2Dchannel modelling will involve multiple channel modellingapproaches or combination of multiple modelling ap-proaches (hybrid) instead of dealing with one modellingapproach. )is will overcome the challenges associated with5G systems’ modelling with accuracy of the developedmodeland complexity trade-off. For example, the multiple slopepath loss models provides a better LOS and NLOS linksapproximation in densified millimeter-wave systems due toirregular cell patterns and blocking features compared to thesingle path loss exponents. )e study can also incorporatemachine learning-based beamforming models for multiusermmwave D2D systems in various LOS and NLOS envi-ronments with time-varying scenario [70].

7.3. Clustering of D2D Users. Clustering can be applied inmmwave D2D communication for D2D devices that areclose to each other to offer energy saving through common

resource sharing.)eD2D clustering can help incorporatingphysical relations and social interactions among the D2Duser terminals.)e heterogeneous network scenario throughradio resource sharing between other devices. )e D2Dclusters can also help in reducing signaling traffic and offerimproved energy performance than the traditional cellularsystems [71, 72]. However, most of the studies in D2Dclustering, such as in [73], have considered D2D commu-nication forming clusters by using Wi-Fi Direct.

For mmwave D2D communication, clustering has beenemployed for performance analysis in mmwave D2D com-munication in terms of SINR outage probability, coverageprobability, and area spectral efficiency [74–76] and coverage.)erefore, there is need to extend D2D clustering for mmwaveD2D communication by considering the performance metricssuch as energy consumption, energy efficiency, packet lossratio, and delay in a static and dynamic environment.

7.4. Green Communications. )e main challenge of 5Gmmwave D2D networks will be mobile D2D devices’ energyconsumption. )e key objective is enhancing the energyefficiency by increasing the D2D rate or decreasing thepower consumed. Energy harvesting is a promising solutionto enable mobile devices user equipment whose batterycapacity is limited to harvest energy to prolong battery life.)erefore, the deployed mmwave D2D networks will requirean analysis of energy efficiency resource allocation algo-rithms for an integrated mmwave D2D network by incor-porating energy harvesting.

)e optimization function for energy harvesting mmwaveD2D networks will consider the constraints such as batterycapacity and QoS and transmit power threshold. )e offlineand online resource allocation optimizations problems withcausal knowledge should also be taken into consideration. Inaddition, energy-efficient sleep/wake up eNB mechanismbased on Q-learning can be designed and incorporated toD2D communication in mmwave pico cells [77].

7.5.UserMobility. Most of the studies that have been carriedon millimeter-wave device-to-device communication haveconsidered static environment for cellular and D2D users.)is scenario might work well for indoor users, where thereis minimal mobility, but for outdoor network scenario, theassumption might not hold. )erefore, there is need toconsider user mobility for the resource allocation optimi-zation mechanisms to maximize the D2D sum rate andenergy efficiency and minimize the latency and inter-D2Dpairs’ interference.

8. Conclusion

)e paper has given a review of resource allocation inmillimeter-wave D2D networks based on a new classificationconsisting of six different mathematical techniques asdemonstrated in the taxonomy. It was found that most ofthese techniques can offer better results even though they arebased on assumptions to reduce their computationalcomplexity during simulation. In practical systems, some of

Mobile Information Systems 13

these assumptions cannot hold for deployment of D2D pairsin the millimeter-wave band. In addition, these studies havemajorly dealt with single-cell network scenarios which needsan extension to multicell networks. )e multicell scenariocan then be analysed by considering energy efficiency,throughput, latency, and connection density performancemetrics. )e survey has also pointed out the challenges thatare hindering attainment of optimal results with all thefeatures of a practical network scenario incorporated in thestudy. )e open research issues that should be exploredfurther by the research community to achieve an improvedperformance in device-to-device networks in the millimeter-wave band have also been given for a better performancemmwave D2D implementation.

Conflicts of Interest

)ere are no conflicts of interest for this publication.

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