TDMA Scheduling and Channel Assignment for Cognitive Tactical Networks

Ossama Younis, David Shallcross, Latha Kant, Kenneth Young

Applied Communications Sciences (ACS), Inc. Piscataway, NJ, USA

{oyounis,dshallcross,lkant,kcy}@appcomsci.com

Charles Graff§ and Mitesh Patel¥ U.S. Army CERDEC

¥ Aberdeen, MD, USA § Ft. Monmouth, NJ, USA

{Charles.graff,mitesh.patel}@us.army.cerdec

Abstract—Shortage of spectrum constrains military

applications that need to support different classes of traffic. In this work, we study efficient utilization of limited spectrum and propose joint time division multiple access (TDMA) MAC scheduling and channel assignment. First, we formulate the problem as a linear program and propose a centralized algorithm that utilizes the concept of “independent link sets” for scheduling slots and allocating channels. Compared to previous work, our approach supports partial flow loss and allows prioritization of traffic flows. Next, we propose extensions to the distributed USAP MAC protocol (widely used in military applications) to jointly assign TDMA slots and channels to links. Our extensions allow USAP to minimize either slots or channels used in the schedule; a function that is needed for dynamic behavior of future SDR/cognitive radios. We implement our scheduling algorithms in the context of a network design tool (NEDAT) and evaluate their performance using realistic mission scenarios.

Index Terms—cognitive networks; MANET design; TDMA scheduling; channel assignment; cross-layer optimization; military applications.

I. INTRODUCTION With the increasing scale and needs of military applications

that require different priority levels of communications, there is a growing need for spectrum, which is currently rare [11]. Therefore, new intelligent mechanisms are needed to efficiently exploit available spectrum to improve the network throughput. Exploiting available spectrum may occur at different levels in the protocol stack. One option is to have a centralized broker in the network to monitor spectrum usage and divide unused channels among users or groups of users. This option is most efficient in terms of channel utilization but less practical in terms of deployment in ad-hoc networks. Another option is to associate channel assignment with routing, which is also efficient in terms of channel assignment but requires changes to existing routers. A third option is to allow the users to use the available spectrum dynamically while setting the TDMA schedules. We focus on the third option in this work because: (1) it fits the dynamic nature of ad-hoc networks in military applications, and (2) it is most flexible in terms of deployment. Note, however, that this option is typically less efficient in terms of channel utilization than the other two options because conservative decisions have to be made to account for its distributed nature.

We consider military applications in which different types of radios are used (e.g., satellite, WNW, SRW). Flows may also be prioritized based on the sending or receiving entities. We propose a centralized algorithm based on linear programming that jointly assigns slots and channels to active links (one-hop communications). The proposed formulation and solution use the concept of “independent link sets” and can prioritize traffic based on user input to allow partial flow loss (traffic priorities are typically set and fixed prior to node deployment). This solution can be beneficial in infrastructure-based domains. We then propose extensions to the distributed USAP MAC protocol [2], which is popular in military applications. The purpose is to arbitrate among available channels and assign channels to links according to an optimization objective. Objectives include minimizing the used number of TDMA slots or minimizing the used channels in the neighborhood. Such capability is essential for enabling adaptive behavior of future software-defined radios/cognitive radios (e.g., GNU [4] or WARP [5]). The distributed algorithm is beneficial to infrastructure-less, highly mobile ad-hoc networks. We evaluate our algorithms in the context of our network design tool (NEDAT [1]) and show performance metrics, such as traffic losses (throughput/utilization) and channel usage.

The rest of the paper is organized as follows. Section II briefly surveys related work and gives an overall picture about scheduling and MAC design. Section III introduces our system model, problem formulation, and centralized MAC algorithm that is proposed in this work. Section IV describes our extensions to the distributed USAP MAC algorithm and Section V compares it with the centralized MAC approach on realistic mission scenarios. Finally, Section VI concludes this work and provides directions for future work.

II. RELATED WORK Several attempts were made to perform MAC access with

channel assignment (some techniques are presented as part of surveys on cognitive radios [6] and [7]). Most of the proposed algorithms attempted to extend the capability of IEEE 802.11 standard ad-hoc mode operation to support multi-channel systems or the emerging new standard for IEEE Wireless Regional Area Networks (WRAN, 802.22). One example is POMDP [8] framework, which proposes distributed techniques to allow secondary users to independently search for spectrum opportunities without coordination modeled using a Partially

978-1-4673-3/12/$31.00 ©2013 IEEE978-1-4673-3/12/$31.00 ©2013 IEEE

Observable Markov Decision Process (POMDP). Its primary drawback is the need for stationary probabilities that are difficult to obtain in highly mobile settings. Another approach is DDMAC [10], which extends WLAN user capability to exploit under-utilized channels. It is simple to implement and deploy, though not in TDMA-based networks. One more general approach for both CSMA- and hybrid CSMA/TDMA-based networks is C-MAC [9], which proposes using control (rendezvous) channels to synchronize traffic transfer. C-MAC does not provide particular implementation details, especially on how to avoid conflicts in selecting rendezvous channels. Yu et al. [14] address channel assignment and link scheduling in the context of wireless mesh networks. F. Hou studied the problem of channel utilization using a general network model [16]. Our work, however, focuses more on the military network/data model. Xian and Lu [15] study joint channel assignment and link scheduling in the context of a general wireless mesh networks.

Our work focuses on pure TDMA-based networks for military applications in which prioritizing traffic is needed and topology is typically divided into hierarchical subnets (node clusters). We uniquely address the problem from a network-design perspective and propose centralized and distributed solutions. We also study how a typical TDMA-based MAC protocol (e.g., USAP [2]) can improve throughput when augmented with channel arbitration mechanisms.

III. JOINT TDMA SCHEDULING AND CHANNEL ASSIGNMENT

A. System Model We assume the following about the network settings in a

military environment: Time consists of a sequence of frames, each of which is divided into a number of slots. A number of frequency channels (which we refer to as “channel set”) are available for every domain (or subnet). At any given slot, each node can at most transmit on one channel, or receive on one channel, or be idle. Nodes can freely change channel from slot to slot. We assume that detecting transmission on any available channel is feasible for all nodes. Different transmissions on different channels do not conflict. Conflicts between potential transmissions are determined by explicit SINR calculations. The network may be divided into multiple subnets (node clusters). A link lies in a single subnet, but some nodes (border gateways) may lie in multiple subnets. A TDMA MAC scheduler must, in addition to determining at what times the various links are active, also assign them frequency channels for their active times.

B. Centralized MAC Time/Channel Assignment

For centralized MAC we relax our assumption about slots, and allow division of a frame into arbitrary fractions. This is primarily for ease of computation, but could also reflect long-term averages of cyclically varying slot assignments.

We assume a topology containing a set E of links. At any given instant, each link e E can operate on at most one of a specified list Fe of frequency channels. No assumptions are made on the relationships between these lists for different links. Each link also has a rate, or capacity, re, representing the rate of traffic it can carry if the SINR is sufficiently good Adaptation of rate to SINR may be considered for future work.

Let I be the set of simultaneous possible channel assignments. That is, if S is an element of I, S is a set of pairs (e,fe), where fe is in Fe, and such that these assignments do not interfere with each other. We don’t actually know I in advance, but will explore it using a column generation method. Let E(S) be the set of links e that occur in some pair in S. Typically E(S) will be a strict subset of E. We have a set D of demands (flows). For each demand d D we have E(d), the set of links in the route chosen for d, and ad, the amount of traffic for demand d (for example, in bits/second). A TDMA schedule corresponds to specifying for each instant in time a particular simultaneous channel assignment. From the point of view of capacity, however, we only need to give the fraction of time in which each simultaneous channel assignment S is chosen to be active, which we will write as φS. A set of values φS correspond to a valid schedule if they have each link active long enough in total to serve the traffic routed across the link, have a sum of time fractions at most 1, and are non-negative.

IS

Eear

S

ISS

dEedd

SEeSSe

0

1)(:)(:

(1)

To compute a schedule which minimizes the total time fraction spent on the given demands, (“capacity formulation”):

IS

Eear

S

dEedd

SEeSSe

ISS

0

subject to

minimize

)(:)(:

(2)

We also minimize loss per flow based on priority (weight).

Given weights wd and upper bounds Bd on loss for each demand d in D, we define a variable λd to represent the assigned loss fraction for demand d, and solve “loss formulation” linear program (we use the “column generation” method for solving these problems):

DdBIS

Eear

w

dd

S

ISS

dEeddd

SEeSSe

Dddd

00

1

1:subject to

minimize

)(:)(:

, (3)

1. Find an initial set I’ of simultaneous channel assignments, to include all links with any traffic.

2. Repeat, up to iteration limit (user input): a. Solve the current linear program using I’ instead of I. b. Look for new simultaneous channel assignments that

improve the solution, using information from the dual variables of current solution. If no such assignments are found, stop. Otherwise, add them to I’, and return to (2a).

For the initial set of simultaneous channel assignments, we

can use the following algorithm, slightly modified from the centralized algorithm for Collaborative-Max-Sum-Reward [12]. We use the concept of a “feasible” simultaneous channel assignment, as being one for which the SINR at each receiver are within specified limits. We build up a simultaneous channel assignment, picking from a set A, the set of assignments that each could be added to an existing simultaneous assignment S while retaining feasibility.

Algorithm 1. Simultaneous Channel Assignment. Input:

Link set and Reward function Re,f for assigning channel f to link e. Set of frequency channels Fe Output: Simultaneous channel assignment S. E0←E. S← Ø Repeat

Let A = {(e,f): e in E0, f in Fe, (e,f) not in S, {(e,f)} S feasible}. For each (e,f) in A:

Let De,f = # of links g in E such that (g,f) in A, but {(g,f),(e,f)} S not feasible. Let le,f = Re,f / (1+De,f)

Let le = max le,f over all f in Fe, such that (e,f) in A. Let f*e = arg max le,f over all f in Fe such that (e,f) in A. Choose e* = arg max le. Add (e*,f*e) to S. Remove e* from E0

Until A= Ø.

Algorithm 2. Generating Initial Simultaneous Channel Assignments.

Input: Link set E. Requested time fraction ge for each link e = requested bit rate / total bit rate for the link Output: initial simultaneous channel assignment set I. E0 ← E. I ← Ø While (E0 ≠ Ø)

Generate a channel assignment S for link set E0, using rewards Re,f = ge . Let I ← I {S}, E0 ← E0 - E(S).

It is well known that a minimization linear program (also

called the primal problem) has an associated dual linear program which is a maximization problem with the same optimal objective function value. Solving the primal usually provides the solution to the dual. Modifying the primal to

decrease the optimal objective requires that the corresponding modification to the dual make the previous dual optimal solution infeasible.

For the capacity formulation, we use the optimal variables μe from the dual linear program:

Ee

Isrts

a

e

sEeee

Eee

dEedd

0

'1..

max

)(

)(: (4)

Thus, adding an assignment S to I’ may improve the primal solution if the sum of reμe on links e of E(S) exceeds 1. To identify such an assignment, we can just use the channel assignment algorithm with the reward Re,f set to reμe , and include the result if its total reward exceeds 1.

For the loss formulation we use the optimal variables μe and ξ from the following dual linear program:

000

0..

max

)(

)(

)(:,,

DdEe

Ddwa

Isrts

Ba

d

e

dddEe

ed

sEeee

Dddd

Eee

dEedd

(5)

For this, an assignment S may improve the solution if the

sum of reμe on links e of E(S) exceeds –ξ. We can use the channel assignment algorithm with the reward Re,f set to reμe , and include the result if its total reward exceeds –ξ.

If we have multiple subnets which have been assigned channels so that there is no possibility of inter-subnet interference, we may use separate sets of simultaneous channel assignments for each subnet, while retaining common loss variables. This will reduce computation time.

IV. DISTRIBUTED TDMA SCHEDULING AND CHANNEL ASSIGNMENT

We focus on USAP MAC protocol [2] for distributed TDMA scheduling, which has been popular for military network applications. We have implemented USAP as part of our network design tool (NEDAT [1]) in order to use it for verification of the benefits gained by different topology control and routing optimization techniques. Steps of the USAP algorithm for single channel-systems are given below: Step 1: Determine the number of slots needed for each flow that is active at time T. Step 2: Determine the links that need to request slots based on the routing paths. Step 3: Exercise USAP slot assignment procedure along each routing path. USAP does not rely on conflict graphs like IEEE 802.11 MAC, but tries to avoid the exposed station problem.

Below, we describe several significant extensions that we have added to the original USAP algorithm: Pack multiple flows originating from the same source to be in the same slot. This saves significant bandwidth with small-load flows, such as chatting, because slot sizes are typically much larger than such periodic flow sizes. Employ a radio interference model to identify interfering links based on actual SINR computations, rather than using static 2-hop neighborhood information (conflict graphs). Support slot assignment for flows on different links. For channel assignment/link, we propose two optimizations: a. Minimize the channels used to save channels for future

reuse by needy users. This is achieved by setting a policy among all users about the order of channels to explore.

b. Minimize the utilized slots in a frame to save slots for incoming traffic. This is done by exploring across channel before looking for new slots.

The features outlined above provide SDR/cognitive radios

(e.g., [4]) with the ability to change behavior at run-time and not just parameters. We provide pseudo-code of our cognitive USAP (C-USAP) algorithm in .

Algorithm 3. TDMA/Channel Scheduling (C-USAP) Input: Link set E = {ei, 1 ≤ i ≤ number of links} Load per link, path loss between nodes, and set of flows. Output: Map of slot assignment (link, slot, channel) Loop1 on all flows Find the links in the flow i Compute the number of slots needed for each link Loop2 on links in flow i Allocate slots to link based on optimization objective If (allocation was successful for all requested slots) Update neighbor conflict graph Else Exit Loop2 (unsuccessful) End If End Loop2 If (unsuccessful allocation of any link) Roll back all channel/slot assignments to previous links Add flow i to unsuccessful list of flows End If End Loop1

An example of the two modes of operation of C-USAP is

shown in Fig. 1 and Fig. 2. In the figures, “s” denotes a slot, “L” denotes a link, “f” denotes a frequency channel, and “F” denotes a flow. Two flows (F0 and F1) are routed in the network and two channels (f4 and f7) are available. The table on the right side of each figure shows channel allocation on every (slot, link) pair.

V. PERFORMANCE EVALUATION

A. Experimental Settings We consider scenarios which occur in irregular (non-flat)

terrains (TIREM models [13]). Missions consider different number of nodes and run hundreds of flows. Some of the nodes have satellite connections. Most of the nodes are on the ground, while a few (<10) are unmanned aerial vehicles (UAVs). Traffic is generated between random sources and destinations across the field. Five types of traffic are considered and all of them are approximated to constant bit rate (CBR) traffic: video (128 Kbps), audio (16 Kbps), file transfer (FT) (100 Kbps), situation alerts (SA) (120 bps), and chatting (400 bps). Table 1 lists the design parameter values.

0

2

1

3

4

5

L0 L2

L1 L3

L4

L5

s=0,f=4

s=1,f=4

s=1,f=4

s=3,f=4

s=2,f=4

s=4,f=4

F0

F1

Min. channelsL0 L1 L2 L3 L4 L5

s0 f4

s1 f4 f4

s2 f4

s3 f4

s4 f4

Fig. 1. Minimizing channel usage in C-USAP

0

2

1

3

4

5

L0 L2

L1 L3

L4

L5

s=0,f=4

s=1,f=4

s=1,f=4

s=2,f=4

s=0,f=7

s=3,f=4

F0

F1

Min. slots

L0 L1 L2 L3 L4 L5

s0 f4 f7

s1 f4 f4

s2 f4

s3 f4

s4

Fig. 2. Minimizing slot usage in C-USAP

Table 1. Default Design Parameter Values Parameter Value

Mission 358 nodes. 250+ flows of 5 types (all CBR). Free-space path-loss model. Random Waypoint mobility

Radios/node Four radio interfaces per node. Radio data Tx power=100 W. Rx sensitivity=-100 dB

Available # of frequency channels=12 Topology One domain. Minimum connectivity. Link capacity

=5 Mbps. Minimum spanning tree topology. Routing Link-state min-hop unicast routing. MAC TDMA MAC for all radio interfaces. Frame

duration=100 msec. Number of slots/frame=15

B. Evaluation Results We evaluate the performance of our algorithms in terms

of three metrics: (1) throughput: the total bits per second from the demand that is successfully scheduled; (2) channel usage: the number of links using each channel (a measure of channel

reuse); and (3) loss assignment per traffic type, which shows how our algorithms prioritize traffic scheduling.

First, we report throughput across the mission when the number of channels varies from 1 to 20, while the traffic profile is fixed. NEDAT designs the mission as separate snapshots. Fig. 3 and Fig. 4 illustrate that centralized MAC performs significantly better when only one channel is available, and the difference reduces as the available channels increase. Note that some of the traffic flows may start/end during the mission. However, throughput changes are primarily due to mobility.

0

5000000

10000000

15000000

20000000

25000000

30000000

0 1000 2000 3000 4000 5000 6000 7000

Thro

ughp

ut in

bits

per

sec

ond

Mission Progress (seconds)

Throughput, Centralized MAC

Total load

centralized, nf=1

centralized, nf=2

centralized, nf=3

centralized, nf=4

centralized, nf=20

Fig. 3. Throughput results when using centralized MAC with

different number of channels (nf: # flows). Next, we pick two one-minute snapshots of the mission;

one representing high load (epoch 3060) and the other representing low load (epoch 6060). Fig. 5 shows the number of links using each channel at epoch 3060. The centralized scheme shows more uniform distribution of channels among links. An interesting observation is that the second channel is used by the maximum number of links with distributed MAC. This is because the first channel is typically used by the high load video flows, which are very few and go over few links. The second channel is used by many more flows and is reused across the network. Obviously, centralized MAC has much better channel distribution. The same results were observed at low load (epoch 6060).

Finally, we test how the centralized MAC algorithm supports traffic priority. We fix the number of frequency channels to four and vary the weights of traffic types to induce prioritization of loss assignment. With video at high priority, much voice traffic is lost. With voice at high priority, no voice traffic is lost, at slight cost to video traffic. SA and chat traffic are assigned no loss with either of these two sets of weights. With distributed MAC, constructing TDMA schedules makes it easier to enforce priorities than a CSMA-based MAC which manipulates back-off timers at each node (e.g., EDCA function of 802.11e). We imposed priorities for C-USAP by manipulating flow arrivals and scheduling flows on a first-come, first-schedule basis.

0

5000000

10000000

15000000

20000000

25000000

30000000

0 1000 2000 3000 4000 5000 6000 7000

Th

rou

gh

put i

n b

its p

er s

eco

nd

Mission Progress (seconds)

Throughput, Distributed MAC, Minimize frequency usage

Total load

distributed, nf=1

distributed, nf=2

distributed, nf=4

distributed, nf=20

Fig. 4. Throughput results when using distributed MAC with

different number of frequency channels.

0

50

100

150

200

250

300

350

0 1 2 3 4 5 6 7 8 9 10

Num

ber o

f lin

ks

Frequency IDs

Usage of frequency channels, epoch=3060

Centralized

Distributed, min frequencies

Distributed, min slots

Of the 1192 links, 375 had traffic routed on them.

Fig. 5. Links using each frequency channel at high load.

CONCLUSION In this work, we studied efficient utilization of spectrum for

cognitive-radio networks. We proposed joint time division multiple access (TDMA) MAC scheduling and channel assignment. First, we formulated the problem as a linear program and proposed a centralized algorithm that utilizes the concept of “independent link sets” for scheduling slots and allocating channels. Our formulation supports partial flow loss and allows prioritization of traffic flows (which flows to save/drop/reduce rate). Then, we proposed extensions to the distributed USAP MAC protocol (widely used in military applications) to jointly assign TDMA slots and channels to links. Our extensions allow USAP to minimize either the slots or the channels used in the schedule; a function that is needed for dynamic behavior of future SDR/cognitive radios.

We implemented our scheduling algorithms in the context of a network design tool (C-NEDAT) and evaluated their performance using realistic mission scenarios. Results show that significant improvement in performance can be achieved by joint optimization of scheduling and channel assignment.

More work is needed to evaluate if cross-layer optimization is beneficial to other types of applications (primarily, domestic applications).

ACKNOWLEDGMENT This work was sponsored by U.S. Army CERDEC under

contract DAAD-10-01-C-0062. The authors thank the entire project team of C-NEDAT at ACS and CERDEC for their diligence in designing and implementing C-NEDAT, and for their insightful feedback.

All the data used in the experiments about radio properties are made up by the authors and are not part of any actual military data.

REFERENCES [1] L. Kant, K. Young, O. Younis, D. Shallcross, K. Sinkar, A. McAuley,

K. Manousakis, K. Chang, and C. Graff, “Network Science Based Approaches to Design and Analyze MANETS for Military Applications,” IEEE Comm. Mag., 46 (11), Nov. 2008, pp. 56-61.

[2] D. Young, “USAP: A unifying dynamic multichannel TDMA slot assignment protocol,” in Proc. of the IEEE MILCOM Conference, 1996.

[3] ANSI/IEEE Std 802.11, 1999th ed., International Standard ISO/IEC8802-11, part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications.

[4] B. Le, T. W. Rondeau, and C. W. Bostian, “Cognitive Radio Realities,” (Invited paper) Wireless Communications and Mobile Computing, John Wiley & Sons, 7(9), Nov. 2007, pp. 1037 – 1048.

[5] Rice University Wireless Open-Access Research Platform, http://warp.rice.edu/.

[6] I. Akyildiz, W.-Y. Lee, M. Vuran, and S. Mohanty, “NeXt generation/ dynamic spectrum access/cognitive radio wireless networks: a survey,” Computer Networks, 50(13), Sep. 2006, pp. 2127-2159.

[7] I. Akyildiz, W.-Y. Lee, and K. Chowdhury, “CRAHNs: Cognitive radio ad hoc networks,” 7(5), July 2009, pp. 810-836.

[8] Q. Zhao, L. Tong, A. Swami, Y. Chen, “Decentralized cognitive MAC opportunistic spectrum access in ad hoc networks: A POMDP Framework,” IEEE Journal on Selected Areas in Communications, 25(3), Apr. 2007, pp. 589–600.

[9] C. Cordeiro, K. Challapali, “C-MAC: A cognitive MAC protocol for multi-channel wireless networks,” Proceedings of the IEEE DySPAN Conference, Apr. 2007.

[10] H. Bany-Salameh, M. Krunz, and O. Younis, “Cooperative Adaptive Spectrum Sharing in Cognitive Radio Networks,” IEEE/ACM Transactions on Networking, 18(4), Aug. 2010, pp. 1181-1194.

[11] FCC, Spectrum policy task force report, ET Docket No. 02-135, 2002 [12] C. Peng, H. Zheng, B. Zhao, “Utilization and fairness in spectrum

assignment for opportunistic spectrum access,” ACM/Springer Mobile Networks and Applications (MONET), 11(4), Aug. 2006, pp. 555-576.

[13] ALION Science & Technology, “TIREM,” http://www.alionscience.com.

[14] H. Yu, P. Mohapatra, X. Liu, “Channel assignment and link scheduling in multi-radio, multi-channel wireless mesh networks,” ACM MONET, Vol. 13, No. 1-2, Apr. 2008.

[15] L. Xian and J. Luo, “Joint channel assignment and link scheduling for wireless mesh networks: Revisiting the partially overlapped channels,” in Proceedings of IEEE PIMRC, 2010.

[16] F. Hou, “Dynamic Channel Selection in Cognitive Radio Network with Channel Heterogeneity,” in Proceedings of IEEE GlobeCom, Miami, FL, Dec. 2010.