Energy Aware Uplink Transmission in

Cellular Networks with Cooperating Base Stations

Efstathios Katranaras

Research Fellow, C.C.S.R., e-mail: Efstathios.Katranaras@surrey.ac.uk

Abstract

An analytical formula is provided to evaluate the performance in the uplink of cellular networks when joint processing is enabled

among limited number of base stations. Focusing on user transmission power allocation techniques to mitigate inter-cluster

interference we investigate the system's spectral-energy efficiency trade-off. The paper addresses the gains in both cell throughput and

transmissions energy efficiency due to the combined strategies of base station cooperation and user power management. Furthermore,

the effect of the propagation environment and of key network design parameters on the overall system performance is assessed.

Keywords: CoMP Wireless Networks, Base Station Cooperation, Energy Efficiency, Uplink Performance.

1. Introduction

Communication systems' increasing demand for high quality

services leads industry into aggressive radio spectrum usage.

Significant losses in throughput and fairness degradation

though occur due to increased amount of inter-cell

interference (ICI) in current cellular systems. That has

rendered Coordinated Multi Point (CoMP) a promising

technology to mitigate and even exploit ICI through signal

joint processing, reception and/or transmission at Base

Stations (BSs). CoMP has the potential to boost spectral

efficiency and to provide more homogeneous user data rates.

On the other hand, energy consumption of communication

technologies is of great interest recently for both

environmental and economical reasons. The energy efficiency

assessment of auspicious technologies like CoMP will serve

as a useful tool to investigate the trade-off between the gains

in throughput and the energy consumption in cellular

networks. CoMP requires additional infrastructural cost, low

latency backhaul links between BSs, signalling overheads and

signal processing at the transceivers end. However, it provides

the potential to save energy due to less signal power needed

from the Signal to Interference plus Noise Ratio (SINR) gains

offered. This work targets to identify efficient User Terminal

(UT) transmit power allocations to conserve energy and

improve performance in systems with cooperating BSs.

In practical systems, only a limited number of BSs can

cooperate in order for the inter-base communication overhead

to be affordable. Numerous works recently address that

clustered cooperation scheme. In [1], the clustered joint

processing strategy was adopted and various cluster isolation

schemes were investigated. However, the focus of the past

research is restricted mostly in the evaluation of the

throughput performance. In this paper we move one step

forward and considering the uplink of a cellular system we

address the effects of the CoMP technology on energy

consumption. Then we investigate in depth the trade-off

between the achieved spectral efficiency and the potential

energy gains due to energy aware transmission. Extended

results and theoretical analysis of this work will appear in [2].

2. Technical Approach

The uplink of a planar cellular array is considered. BSs, each

one at the centre of a hexagonal cell, are uniformly distributed

over the 2D grid. UTs are uniformly distributed over each cell.

The network is divided into sets of cells, grouped together to

form clusters of size Q . A Joint Processor (JP) in each cluster

combines all the UTs' received signals of that cluster. An

inter-cluster interference allowance scheme with no spectral

isolation between clusters is considered and thus, all UTs are

allowed to exploit the full resources allocated to the system.

Every BS experiences inter-cluster interference due to

transmitted signals from UTs in other clusters. For the

theoretical analysis, a power-law path-loss model which can

capture the shadow fading effect is considered along with a

generalised multipath fading model [3]. Symmetry among all

clusters of cells is assumed.

The spectral-energy efficiency trade-off has become recently

an important performance measure for future solutions in the

design of a cellular system. In this work the per cluster

ergodic achievable sum rate (in bits/sec/Hz) is regarded to

evaluate the spectral efficiency of the system [1]:

1

Desired Received Power at cell 1

Noise Interference from other clusterslog

Q

q

qR

(1)

where inter-cluster interference, can be assumed a sum of

complex Gaussian inputs and thus, can be viewed as an

additional AWGN component at the BSs. On the other hand, a

general model for the additional energy required to maintain a

successful BS cooperation scheme is discussed in [2]. In this

work, our interest lies on evaluating the efficiency of UTs

power management strategies under CoMP and hence, the

system average transmit energy efficiency is identified as:

Tx [bits/Hz/Joule]Total Cluster Transmitted Power

R

EE (2)

An efficient way to reduce both inter-cluster interference

and system transmit energy consumption is to perform power

control on the UTs' transmissions according to the interference

Fig. 1 UT Power management example (Q = 3). (a) CPA, (b) EPA,

(c) UT power allocation as function of UT position within cell.

Fig. 2 SE versus transmit EE for various power management

strategies and cluster sizes.

each one causes on neighbouring clusters considering that

UTs in a disadvantageous channel situation contribute less on

the total sum rate. Following the insights from the

optimisation analysis in [4], assuming distance as the main

cause of channel degradation over a large time scale, we adopt

a Cell-based UT Power Allocation profile (CPA) which can

provide a simple and tractable, yet insightful analysis of the

power management problem (Fig.1a). In CPA scheme, the

transmitted power of a UT becomes a linear function of

distance s from its associated BS (Fig.1c). A minimum power

constraint ensures that all UTs are able to perform their basic

and emergency communication needs at any time. Since inter-

cluster interference is mostly originated from UTs located

closer to cluster edges, another meaningful strategy would be

to manage only the power of UTs that are at cluster edges.

This cluster-Edge-based Power Allocation scheme (EPA) is

illustrated in Fig. 1b. EPA shall achieve higher sum rates than

CPA since UTs at the centre of the cluster, which experience

less inter-cluster interference, can achieve high SINR. On the

other hand, as the cluster size increases, there will be a lower

percentage of UTs in the cluster managing their transmitting

power and the energy savings will be reduced. Thus, it

becomes very interesting to compare these power schemes and

evaluate which effect becomes stronger in that case.

3. Key Results and Discussion

For interpreting the results into real-world systems we assume

the practical scenario described in [4]. We consider UT power

constraints of 100mW and 200mW. Numerical results are

derived for a planar system with varying clusters size,

considering a toroidal-like model to avoid edge effects. 20

uniformly distributed UTs over each cell are considered.

Figure 2, illustrates the performances of the CPA and EPA

schemes in comparison to the one achieved without any power

allocation strategy (no-PA scheme). Inter-Site Distance (ISD)

of 2Km and path loss exponent of 3 is considered. Power

allocation parameters α1 = 0.1 and α2 = 0.8 were observed to

provide substantial performance gain [4]. The benchmark

performance of the system under global cooperation (GC) is

also illustrated. It is observed that both CPA and EPA achieve

similar sum rates and outperform the no-PA scheme since the

power management and the resulted mitigation of the inter-

cluster interference translate into both increased spectral and

energy efficiency. It also becomes apparent that CPA is the

most energy efficient scheme. That is explained from the fact

that in CPA we restrain the power of cell edge UTs whose

rates are anyway exceptionally low when summing up to the

cell sum rate. Finally, it is worth noting that higher

cooperation, in the form of higher cluster size, provides gain

in both SE and EE for CPA and no-PA schemes. On the other

hand, with increasing Q , the EE of EPA boils eventually

down to the EE of no-PA since the power conservations which

are performed only at a portion of the cluster edge cells

become minimal. Extended results and insights about the

power schemes and the optimal ISD under clustered

cooperation will appear in [2].

4. Summary of the work, potential impact & Conclusion

In this paper the performance gains from power control in the

uplink of cellular networks with cooperating BSs are assessed.

A strategy of non-greedy UTs transmitting at maximum when

in good channel condition could prove very efficient in mobile

and delay tolerant applications. This work aims to be a starting

point for a further research on defining a generalised

framework for spectral-energy efficiency trade-off evaluation

in CoMP enabled cellular systems.

Key References

[1] E. Katranaras, M.A. Imran, R. Hoshyar. “Sum Rate of Linear Cellular

Systems with Clustered Joint Processing,” IEEE 69th Vehicular

Technology Conference, (VTCSpr’09), 2009.

[2] E. Katranaras, M.A. Imran, R. Hoshyar. “Energy Aware Transmission in

Cellular Uplink with Clustered Base Station Cooperation,” to appear in

IEEE 73rd Vehicular Technology Conference, (VTCSpr’11), 2011.

[3] E. Katranaras, M.A. Imran, C. Tzaras. “Uplink Capacity of a Variable

Density Cellular System with Multicell Processing,” IEEE Transactions

on Communications, 57(7):2098-2108, July 2009.

[4] E. Katranaras, D. Kaltakis, M.A. Imran, R. Hoshyar. “Interference

Allowance in Clustered Joint Processing and Power Allocation,” 6th

International Wireless Communications Mobile Computing Conference, (IWCMC’10), 2010.

 

How MIMO compares with SISO System in terms of Energy-Efficiency ? Fabien Héliot

Research Fellow, C.C.S.R., e mail: - F.Heliot@surrey.ac.uk

 

Abstract

Energy efficiency (EE) is becoming a topic of prime interest in communication. Having recently derived a closed-form appro-ximation of the EE-spectral efficiency (SE) trade-off over the multi-input multi-output (MIMO) Rayleigh fading channel, we here utilize this expression for assessing analytically the EE gain of MIMO over single-input single-output (SISO) system for two types of power consumption models (PCMs): the theoretical PCM, where only the transmit power is considered as consumed power; and a more realistic PCM accounting for the fixed power component and amplifier inefficiency. Our analysis unfolds the large discrepancy between theoretical and practical results; in theory, the EE gain increases both with the SE and number of antennas; whereas in practice, transmitting with more than two antennas is energy inefficient.

Key words: Energy Efficiency, MIMO system, Rayleigh channel

1. Introduction

Energy efficiency (EE) can be seen as a mature field of research in communication, at least for power-limited applications such as battery-driven system, wireless ad-hoc or sensor networks. However, in the current context of growing energy demand and increasing energy price, it is a new frontier for communication network. Indeed, communication network operators are currently driving the research agenda towards more energy efficient network as a whole in order to decrease their ever-growing operational costs. The first signs of this trend can be seen in the development of future mobile systems such as long term evolution-advanced (LTE-A).

The EE of a communication system is obviously closely

related to its power consumption and the main power-hungry component of a traditional cellular network is the base station (BS). In most of the theoretical studies, the consumed power of a transmitting node such as a BS is equal to its transmit power, whereas in reality, the consumed power is the addition of two main elements: the fixed consumed power, which accounts for cooling, processing, etc.; and the variable consumed power. Thus, in order to get a full picture of the total consumed power in the system and evaluate fairly its EE, a proper power consumption model (PCM) must be defined for each node, such as the one recently proposed in [1].

The EE being a ratio between the rate and the power, the EE gain between two systems can either be the result of a system providing a better rate than the other system for a fixed transmit power, or a lower power consumption for a fixed rate, when both systems are affected by the same level of noise and occupy the same bandwidth. In other words, the EE gain is either due to an increase of SE or a decrease in consumed power. Multi-input multi-output (MIMO) system is already well-known to be very effective for the former [2], thus as in [3], our analysis is focused on the latter, more precisely on how efficient is MIMO for reducing the consumed power over the Rayleigh fading channel.

2. Technical Approach

Assuming a linear PCM at the transmitter side, the energy per bit of a communication system as a function of its SE, i.e. its EE-SE trade-off, can be formulated as [4]

( )1b 0 /PE N f S tP− R⎡ ⎤= Δ +⎣ ⎦ , (1)

where ΔP and P0 are the slope and constant term of the PCM, N is the noise power at the receiver, R is the achievable rate, t is the number of transmit antennas and /S C W= is the spectral efficiency, i.e. the ratio between the capacity C and the bandwidth W. According to the Shannon’s capacity theorem [5], the capacity can be formulated as C = Wf(γ), where [ ] [ ]: 0, 0,f Sγ ∈ +∞ ∈ +∞ and γ is the received signal-to-noise ratio of the system, and we define

[ ] [ ]1 : 0, 0,f S γ− ∈ +∞ ∈ +∞ as the inverse function of f. Note that ΔP = 1 and P0 = 0 for the theoretical PCM.

In order to evaluate how MIMO compares with SISO system in terms of EE, we define the EE gain of MIMO over SISO as GEE = Eb,SISO/Eb,MIMO, where Eb is given in (1). We obtain after intricated derivations the theoretical and practical MIMO/ SISO EE gain limits in the low and high SE-regime as [4]

( ) ( )( ) ( )

0EE,Th

1 11 12EE,Th 1 2

Sm

G r

G S eζββ φβ β

−⎛ ⎞ ⎛−− ⎜ ⎟ ⎜−∞ ⎝ ⎠ ⎝

≈

≈ −1 ⎞− ⎟⎠

(2)

and

( ) ( ) ( )( )

0EE,Pr

1

EE,Pr EE,Th

1/

1 1 sgn 1 0

Gr t

G m G

ϕϕ

ϕ t−∞ ∞

+≈

+

≈ + − − +⎡ ⎤⎣ ⎦

, (3)

respectively, where r is the number of receive antenna, m = min{t,r}, p= max{t,r}, β = p/m, ζ = sgn(ln(r/t)), and sgn(x) = -1,0 or 1 if x<0, x=0 or x>0. In addition, φ = 0.57721… and φ = ΔPPSISO/P0, where PSISO is the SISO transmit power. 

 

SSSS

S

S

S

S

Fig. 1. MIMO/SISO EE gain against the number of antenna elements n = r = t for the theoretical PCM.

3. Key Results and Discussion

Comparing the second equation of (2) with the second equation of (3) unveils an interesting paradox in the high SE regime; the second equation of (2) clearly indicates that the theoretical EE gain increases with the SE (exponentially) as well as the number of antennas. Whereas the second equation of (3) shows that the EE gain decreases with the number of transmit antennas when m > 1 and a linear PCM is considered and, consequently, that t = 2 is the most energy efficient number of transmit antennas in the high-SE regime when r > 1. Also, (3) reveals that if φ < 1 then MIMO cannot be more EE than SISO, when m > 1.

In Figs. 1 and 2, we plot the EE gain of a n x n MIMO over SISO system as a function of the number of antenna elements n = t = r for the theoretical PCM and the linear PCM of [1], respectively. We set PSISO = 2Pmax, where the numerical values for Pmax, ΔPPSISO and P0, can be found in Table 7 of [1] for each type of BS. The results in Fig. 1 confirms that the theoretical EE gain increases with the SE as well as the number of antennas. Concerning the latter, this behavior is directly related to the behavior of the term (1-1/m) in (2), which increases as m = n increases. Furthermore, they clearly show that our limits for the theoretical EE gain at low and high SE in (2) are accurate. On the one hand, the curve of G0

EE,Th tightly matches the curve of GEE for S = 10-2. On the other hand, the curve of G∞

EE,Th tightly fits GEE for S = 40 in Fig. 1 when n < 6. Note that G∞

EE,Th in (2) has been derived by assuming that S >> m = n, however, this assumption weakens as n > 5 and a gap between G∞

EE,Th and GEE appears. In contrast with the results obtained in Fig. 1 for the theoretical PCM, the results in Fig. 2 indicate that only a very limited EE gain, i.e. no more than 1.31, can be achieved in the Macro BS scenario when n = 2. In all the other scenarios, SISO is more EE than MIMO. The results also indicates that GEE increases with the SE but decreases with the number of transmit antennas when m > 1, which was forseen by our analytical result in (3). Thus, the most desirable number of transmit antennas in terms of EE is two, as it is shown in Fig. 2.

  Fig. 2. MIMO/SISO EE gain against the number of antenna

elements n = r = t for different linear PCMs.

As in the theoretical case, our limits for the practical EE gain at low and high SE, i.e. G0

EE,Pr and G∞EE,Pr, tightly match GEE

for S = 10-2 and S = 40, respectively. Moreover, they clearly act as lower and upper bounds for GEE such that the maximum practical EE gain is given by the second equation of (3), where the ratio φ can be a simple criteria for assessing whether or not to use MIMO for EE purpose. For instance, we obtain that φ 1.63, 0.75 and 0.28 for Macro, Micro and Pico

Ss, respectively, by computing φ for each BS type of Fig. 1. his confirms that if φ < 1 then SISO is more EE than MIMO.

BT 4. Summary of the work, potential impact & Conclusion

Our analysis have shown the large discrepancy between the theoretical and practical MIMO/SISO EE gains; in theory, MIMO has a great potential for EE improvement over the Rayleigh fading channel; in contrast, when a realistic PCM is considered, a MIMO system with two transmit antennas is not necessarily more EE than a SISO system and utilizing more than two transmit antennas is energy inefficient, which is consistent with the findings in [3] for sensor networks.

Key References

[1] G. Auer et al., “D2.3: Energy Efficiency Analysis of the Reference Systems, Areas of Improvements and Target Breakdown,” INFSO-ICT-247733 EARTH (Energy Aware Radio and NeTwork TecHnologies), Tech. Rep., Nov. 2010.

[2] M. Dohler, “Virtual Antenna Arrays,” Ph.D. dissertation, King’s College London, University of London, Nov 2003.

[3] S. Cui, A. J. Goldsmith, and A. Bahai, “Energy-Efficiency of MIMO and Cooperative MIMO Techniques in Sensor Networks,” IEEE J. Sel. Areas Commun., vol. 22, no. 6, pp. 1089–1098, Aug. 2004.

[4] F. Héliot, M. A. Imran and R. Tafazolli, “On the Energy Efficiency-Spectral Efficiency Trade-Off over the MIMO Rayleigh Fading Channel,” submitted to IEEE Trans. Commun., Apr. 2011.

[5] C. Shannon, “A Mathematical Theory of Communication,” Bell Syst. Tech. J., vol. 27, pp. 379–423, 623–656, July-Oct. 1948.

 

Intelligent task allocation in collaborative multi-hop wireless networks

Yichao Jin

Year 3 PhD Student, C.C.S.R., e-mail: yichao.jin@surrey.ac.uk 1

Abstract

Merging applications in Multi-Hop Wireless Networks (MHWNs) that require rich-resource support today may well be capable of supporting in future MHWNs via resource sharing and node collaboration. A computational intensive program can be partitioned into multiple small tasks and executes each task concurrently on independent nodes. However, multi-hop wireless communication is inevitable which could have an adverse effect on distributed processing. Thus, in this paper, an adaptive intelligent task mapping together with a scheduling scheme is proposed to provide the required Quality of Service as well as to extend the network lifetime. This solution enables efficient parallel processing in a way that possible node collaborations with cost-effective communications are considered. Thereby, it saves bandwidth and energy, extending the network lifetime, and reducing latency. Key words: Task allocation, Multi-hop wireless network, Node collaboration

1. Introduction

Node collaboration plays a very important role in future Multi-hop Wireless Networks (MHWNs) like sensor networks and mobile ad-hoc networks, which can share the heavy work load among nodes to achieve balanced energy consumption as well as accelerating the processing speed. In addition, through appropriate task allocation, it is possible to avoid wasting transmissions on redundant information. Computational functions like detecting an event, estimating and analysing parameters, fusion modelling can be carried out within the network so that, instead of transmitting all the raw data across a MHWN, only the computational results are delivered to the end user. Thereby, it saves bandwidth and energy, extending the network lifetime, and reducing latency. Early applications in this area include video-based streaming and tacking, low latency multi-player gaming with portable smart devices, seamless robot collaboration in cyber-physical systems, etc.

Since most of the nodes in MHWNs are battery powered, an energy conservation and balanced energy consumption design is desirable to prolong the node lifetime. Meanwhile, many real-time applications are highly sensitive to end-to-end delays. Thus, one of the challenges for task allocation in MHWNs is to use the energy resources as efficiently as possible while providing the diverse Quality of Service (QoS) required by the system.

Numerous studies have been conducted into task mapping and scheduling in parallel and distributed systems. In wired networks, since nodes are often connected with dedicated and cost-effective links, communication does not have a major impact on energy or time cost. However, the situation in a MHWN is quite a contrast, as the expensive multi-hop radio communication incurs additional costs to distribute the tasks. On the other hand, due to the communication related issues such as interference, research in wireless networks mainly considers simple network scenarios, such as single-hop and homogeneous networks. In addition, they mainly employ heuristic methods to tackle this issue. However, the majority of heuristic algorithms use step-by-step optimization which is non-backtracking. In other words, once a decision is made it cannot be changed even if it is found to be inappropriate at a

later stage of execution of the algorithm. Therefore the search for solutions could be misguided in such cases, especially in complex MHWNs with communication issues that are potentially prone to such instances. In contrast, Genetic Algorithms (GAs) are popular in optimization problems for their high probability in obtaining a global optimum.

Therefore, in this paper, a GA is adopted as optimization tools to address the task allocation and scheduling problem in MHWNs. The proposed Intelligent Task Allocation and Scheduling (ITAS) approach aims to balance the energy consumption over collaborative nodes, as well as to provide sufficient processing power to guarantee the application latency demands in various MHWN environments.

2. Technical Approach

We use k rounds of a Direct Acyclic Graph (DAG) to model an application, where k=1, 2 … depends on how long the application lasts. There are directed edges among the tasks which forms a topological execution order of the tasks in the DAG. A task at the starting point of the edge has to occur earlier than the one at the ending point. The network is modelled by randomly deployed heterogeneous nodes which are connected with multi-hop wireless links. The objective is to assign all tasks in the DAG to the nodes in a way that the application performance can meet the design goal which is maximising the network lifetime under deadline constraints.

In GAs, a complete solution of a problem is called a chromosome. Thus, the ITAS algorithm starts with a population of encoded chromosomes. Each chromosome is encoded by a 2-by-n task-node mapping matrix, where n is the total number of the tasks in the DAG. The communication cost in the DAG is modelled by an Edge matrix. Since the communication task pairs in the Edge could be allocated to nodes that are several hops away from each other in the actual network, a mapping engine is developed which insert routing tasks in the Edge such that the multi-hop communication cost can be included. A hybrid fitness function is developed to combine the two fitness parameters (network lifetime and application execution time) together and performs parallel optimization to achieve the design objectives. Details of the

 

encoding process and this hybrid fitness function can be found in [1]. The chromosomes are further evolved to obtain better solutions via the GA operations, In order to guarantee the application QoS requirements, at each generation, certain percentage of the best chromosomes that meet the deadline are inherited from the previous generation. Providing that none of the chromosomes in the population can guarantee the deadline, those with less delay are inherited. The ITAS algorithm repeats itself until it reaches the pre-defined maximum number of iterations or if the best result is achieved. Eventually, the good ’genes’ are accumulated in the offspring chromosomes, whilst the bad ones are eliminated. The best chromosome in the last generation will be chosen as the final solution. A flow chart of the algorithm can be found below:

Fig. 1 ITAS flowchart

Furthermore, in order to speed up the convergence of the algorithm, a Heuristic inspired Intelligent Task Allocation and Scheduling (HITAS) is used to partially guide the initial chromosome creation, where collaborations between nodes that are far away from each other are restricted. Thus the probability of node collaboration with expensive multi-hop communication cost is reduced, and only instances with cost-effective communication links are offered for the GA evolution to produce optimal solutions.

3. Key Results and Discussion

Due to limited spaces, we only present some of the key outcomes in this paper, but more results can be found in [1]. Fig.2 illustrates that HITAS requires less effort (fewer number of population size and generation limit) to output the same results compared with ITAS. Thus, HITAS is more efficient than ITAS. The following results are further compared with two existing algorithms: Greedy [2] and MTMS [3]. In Fig.3, ITAS and HITAS are more adaptive to meet the latency constraints as they follow the deadline much closer compared with the other algorithms. Furthermore, when the deadline constraint is relaxed, ITAS and HITAS achieve significant lifetime improvements as the possibility of workload sharing is increased. In Fig.4, we can observe that the proposed algorithms also perform better than the others as they attempt to involve more of these newly added nodes in order to balance the energy consumption subject to the deadline requirements. Please note that both ITAS and HITAS need a certain learning period to perform the optimization process depending on the complexity of application and the scale of

the network. However, even in very complex scenarios, it is suggested that ITAS and HITAS can still benefit long-term applications and provide reliable and cost-effective solutions.

10 20 30 40 505.05

5.15

5.25

5.35

5.45

5.55

5.65

5.75x 10

5

Population Size

Net

wor

k lif

etim

e(ro

und)

Lifetime(ITAS)

Lifetime(HITAS)

10 20 30 40 505

5.1

5.2

5.3

5.4

5.5

5.6

5.7

5.8x 10

5

Generation limit

Net

wor

k lif

etim

e(ro

und

)

Lifetime(ITAS)

Lifetime(HITAS)

Fig. 2 Effect of changing population size and generation limit

30 40 50 60 70 80 90 100 110 12030

40

50

60

70

80

90

100

Application Deadline(ms)S

ched

ule

exec

utio

n tim

e (m

s)

MTMSITASHITASGreedyDeadline

30 40 50 60 70 80 90 100 110 1201

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6x 10

5

Application Deadline(ms)

Net

wo

rk li

fetim

e (

roun

d)

MTMSITASHITASGreedy

Fig. 3 Effect of altering application deadlines

5 10 15 20 25 30 35 4065

70

75

80

85

90

95

100

105

Number of nodes

Sch

edul

e ex

ecut

ion

time

(ms)

MTMSITASHITASGreedyDeadline

5 10 15 20 25 30 35 400

1

2

3

4

5

6

7

8

9

10x 10

5

Number of nodes

Net

wor

k lif

etim

e (

roun

d)

MTMSITASHITASGreedy

Fig. 4 Impact of changing the number of nodes

4. Conclusion

In future MHWNs, system performance like latency and network lifetime is greatly affected by how various application tasks are mapped to the processing nodes in the network. Parallel processing among collaborative nodes can greatly improve the system efficiency yet it becomes a challenging problem when accommodating the multi-hop communication aspects at the same time. The proposed ITAS algorithm extends the network lifetime by balancing the energy consumption among collaborative nodes, as well as guaranteeing the application QoS requirement through possible parallel processing.

Key References

[1] Y. Jin, J. Jin, A. Gluhak, K. Moessner, and M. Palaniswami, “An intelligent task allocation scheme for multi-hop wireless networks,” IEEE Transactions on Parallel and Distributed Systems, accepted for

publication, expected available online in July, 2011.

[2] V. Tsiatsis, R. Kumar, and M. B. Srivastava, “Computation hierarchy for in-network processing,” Mobile Networks and Applications, vol. 10, no. 4, pp. 505–518, 2005.

[3] Y. Tian and E. Ekici, “Cross-layer collaborative in-network processing in multihop wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 6, no. 3, pp. 297–310, 2007.

Submissions to be sent to e.james@surrey.ac.uk by 5pm on 21 April 2011

Fundamental Trade-off between Energy Efficiency and Spectral Efficiency in

Cellular Networks

Oluwakayode Onireti

Year 2 PhD Student, C.C.S.R., e-mail: O.Onireti@surrey.ac.uk

Abstract

This research work is based on the analysis of the trade-off between Energy Efficiency (EE) and Spectral Efficiency (SE) in the

cellular networks with focus on base station cooperation which is the information theoretic optimal approach in terms of SE. We

obtain our EE-SE trade-off by deriving closed-form approximation (CFA) that allows for expressing the EE in terms of SE. We

considered the theoretical power model which is based only on the transmit power and the realistic power model which incorporates

the backhaul and signal processing powers in addition to the transmit power.

Key words: Energy efficiency, spectral efficiency, base station cooperation, trade-off

1. Introduction

In the past, communication network evolution has mainly

been driven by spectral efficiency (SE) improvement. In

recent years, the reduction in network energy consumption has

become of great importance for network operators. So has the

importance of the energy efficiency (EE) as a metric for

network performance evaluation.

The SE is the traditional metric for measuring the efficiency

of communication systems. It is a measure of how efficiently a

limited frequency resource (spectrum) is utilized. It however

fails to give any insight on how efficiently energy is utilized.

A new metric that provides this insight was introduced in [1],

i.e. the bits-per-Joule (bits/J). The bits/J capacity of energy

limited wireless network was defined as the maximum amount

of bits that can be delivered by the network per Joule it

consumed to do so.

Research work on EE was initially motivated by limited

power applications [1]. Such applications include underwater

acoustic telemetry, wireless ad hoc networks such as sensor

networks, home networks and data networks. Since most of

these systems are operated on batteries, EE is a paramount

factor for designing such networks. The global trend towards

energy consumption reduction has led to the extension of

theEE concept to unlimited power applications, e.g. devices

with constant power supply such as base station (BS) and

fixed relay terminal in cellular networks. Moreover, the

available spectrum resource needs to be efficiently used for

the transmission of information bits and, consequently, the SE

also needs to be taken into account in the design of

communication networks. However, the two objectives of

minimizing the energy consumed in the network and

maximizing the bandwidth efficiency, i.e. SE, are not

achievable simultaneously and, hence, this creates the need for

a trade-off. The EE-SE tradeoff in a cellular architecture

provides a guide for operators to obtain the best operating

point which could either be towards an energy efficient

network or a spectral efficient system.

The Shannon’s capacity theorem illustrates that there exists

a trade-off between bandwidth, transmit power and the coding

strategy implemented to achieve a certain rate R, in other

words, the trade-off between EE and SE.

Our research work is based on the EE-SE trade-off in

cellular network with focus on base station cooperation which

is the information theoretic optimal approach in terms of SE.

In the uplink, we compare the EE-SE performance of BS

cooperation with joint decoding (JUD) at the central processor

with other non cooperative architecture such cell optimum

approach with joint decoding at each cell and the non

optimum approach with single user decoding (SUD) at each

cell. The EE-SE CFA is based on the theoretical power model

which considers the transmit power only and the realistic

power model which incorporates the signal processing power,

backhaul power and all other power losses in the network in

addition to the transmit power.

In [2], we derived EE-SE trade-off CFA for the uplink of

cellular system with BS cooperates by considering the Wyner

model with multi-input multi-output (MIMO) Rayleigh fading

channels between the user terminals and the BS. While in [3]

we extend this to the uplink of the cellular model with

uniformly distributed user terminals.

2. Technical Approach

We derived the EE-SE trade-off expression for BS

cooperation based on the per-cell sum-rate and the total power

consumed per-cell in the uplink by using a CFA. Our CFA

was obtained by exploiting the random matrix theory for

limiting eigenvalue distribution of large random matrices. In

[2] we have showed that for the theoretical power model the

EE-SE trade-off expression of Wyner model with BS

cooperation can be expressed as

(1)

Submissions to be sent to e.james@surrey.ac.uk by 5pm on 21 April 2011

Fig.1 Comparison of our closed-form approximation (CFA), Monte-Carlo

simulation and Low power approximation (LP approx) based on theoretical

power model.

where R is the achievable rate, W is the bandwidth, !0 is the

noise spectral density, κ is the ratio of the horizontal and

vertical dimension of the overall channel matrix H, W0 is the

Lambert function, Cr and Ct are given in [2],ϒ is 1+2α2 and

1+6α2 for the Wyner circular and planar model respectively.

When the realistic power model is considered, we obtain the

effective energy efficiency per cell as

(2)

where l is the number of user terminals per cell, P is the user

terminal transmit power, PM is the total power consumed in

the uplink while using the power model of [4].

3. Key Results

In this section, we evaluate the EE and SE performances of

the linear cellular architecture based on the Wyner model. We

evaluate performance by using the joint user decoding (JUD)

for the case with full BS cooperation and single user decoding

(SUD) for the case with no cooperation..We fix the number of

BSs to 10, the attenuation scaling factor for the Wyner model

to α = 0.4 and noise variance σ2 = 1 unless otherwise stated.

We assume an orthogonal multiple access scheme, e.g.

TDMA, within each cell such that only one user is active

percell at each instance of time. The other parameters used in

our evaluation are listed in Table I of [2].

In Figure 1 we evaluate the EE and SE of the Wyner

model with various antenna combinations using the theoretical

power model. It could be observed that our closed-form

approximation results closely match those obtained through

Monte-Carlo simulation, whereas the low power

approximation approach of [5] is mainly accurate in the low-

SE regime. Increasing the number of antennas at the UT or BS

node results in an increase in the EE and SE of the system

since the slope of the trade-off curve becomes steeper in this

case.

Fig. 2 Comparison of the EE-SE performance of non cooperative BSwith BS

cooperation based on the realistic power model. Parameters:r = 2, t = 2, P =

27dBm.

In Figure 2, we introduce the realistic power model which

incorporates the signal processing and backhaul powers into

our evaluation. The results show that increasing the number of

cooperating BSs results in a loss in EE as no gain in per-cell

sum-rate is achieved by increasing !c beyond three. When the

!c increases then the backhaul power increases at the same

time, thus, leading to a loss in EE. In addition, for very large

!c, non cooperating scheme with single user decoding can

outperform the cooperative scheme with joint decoding over a

significant range of attenuation scaling factor.

4. Summary

We have obtained a closed form approximation for the EE-

SE trade-off in the uplink of cellular network with BS

cooperation. Our closed form approximation closely matches

the Monte-Carlo simulation results.

References

[1] Kwon and T. Birdsall, “Channel Capacity in Bits per Joule”,

IEEEJournal of Oceanic Engineering, vol. 11, no. 1, pp. 97–99, Jan.

1986.

[2] O. Onireti, F. Héliot, and M. A. Imran, “Closed-form Approximation for the Trade-Off between Energy Efficiency and

Spectral Efficiency in the Uplink of Cellular Network”, in Proc.

Eurecom 2011, Vienna Austria, Apr. 2011.

[3] O. Onireti, F. Héliot, and M. A. Imran, “Trade-Off between

Energy Efficiency and Spectral Efficiency in the Uplink of a Linear

Cellular System with Uniformly Distributed User Termninal”,

submitted to IEEE 22nd Int. Symp. Personal, Indoor and Mobile

Radio Communications PIMRC 2011. 2011

[4] A. J. Fehske, P. Marsch, and G. P. Fettweis, “Bit per Joule

Efficiency of Cooperating Base Stations in Cellular Networks”, in

Proc. IEEE Globecom, Miami, USA, Dec. 2010. [5] S. Verdu, “Spectral Efficiency in the Wideband Regime”, IEEE

Transactions on Information Theory, vol. 48, no. 6, pp. 1319–1343, Jun.

2002.

Average Energy Efficiency Contours with Multiple Decoding Policies

Amir AkbariPhD Student

Centre for Communication Systems Research, University of Surrey, Guildford, GU2 7XH

30/6/11

Why Energy Efficiency?

Environmental Incentives

Economic Incentives

2

What do we Propose ?

• Most existing system-level studies on energy efficiency (EE) optimize a single objective

Our approach:

We introduce average energy efficiency contours, allowing us to find a trade-off between:

Energy efficiency

Rate-fairness

Maximum sum-rate

3

Energy Efficiency Metric

System scenario:

Single-carrier, uplink scenario with K transmitters and a single receiver.

kc

kk PP

REE

K

1kkAV EE

K1EE

4

Definition:

Energy efficiency is defined as the number of bits transmitted per joule of energy

The comparison metric used in this study is based on the average energy efficiency of the system

Data rate (bits/sec)

Circuit power + Transmit power (Joules/sec)

Energy Efficiency Contours

R1 (Mbps)

R2

(Mbp

s)

0 0.1 0.2 0.3 0.4 0.50

0.1

0.2

0.3

0.4

0.5

0 0 1 0 2 0 3 0 4 0 50

2

3

4

5

0 0 1 0 2 0 3 0 4 0 50

2

3

4

5

0 0 1 0 2 0 3 0 4 0 50

2

3

4

5

0 0 1 0 2 0 3 0 4 0 50

2

3

4

5

Line of FairnessSlope: +1

0 0 1 0 2 0 3 0 4 0 50

2

3

4

5

B

Line of FairnessSlope: +1

0 0 1 0 2 0 3 0 4 0 50

2

3

4

5

B

Line of FairnessSlope: +1

1

0 0 1 0 2 0 3 0 4 0 50

2

3

4

5

1

B

A

C

Line of FairnessSlope: +1

0 0 1 0 2 0 3 0 4 0 50

2

3

4

5

B

Line of FairnessSlope: +1

1

A

C

Line of Constant Sum-RateSlope: -1

0 0 1 0 2 0 3 0 4 0 50

2

3

4

5

B

Line of FairnessSlope: +1

1

A

C

D

Line of Constant Sum-RateSlope: -1

0.5

0.4

0.3

0.2

0.1

0

0 0.1 0.2 0.3 0.4 0.5

Rate of user 1 (Mbps)

5

Summary

We introduce average energy efficiency contours

Depending on system requirements, it is not always desirable to maximize Energy Efficiency

We show the trade-ff between energy efficiency, rate-fairness and maximum sum rate

For a fixed energy efficiency target, both fairness and maximum sum- rate can be incorporated into the system design

6

Questions ?

7