Resource Allocation for OFDMA-Based Cognitive Radio Networks with Application to H.264 Scalable Video Transmission
© Mohammud Z. Bocus et al. 2011
Received: 21 September 2010
Accepted: 23 February 2011
Published: 15 March 2011
Resource allocation schemes for orthogonal frequency division multiple access- (OFDMA-) based cognitive radio (CR) networks that impose minimum and maximum rate constraints are considered. To demonstrate the practical application of such systems, we consider the transmission of scalable video sequences. An integer programming (IP) formulation of the problem is presented, which provides the optimal solution when solved using common discrete programming methods. Due to the computational complexity involved in such an approach and its unsuitability for dynamic cognitive radio environments, we propose to use the method of lift-and-project to obtain a stronger formulation for the resource allocation problem such that the integrality gap between the integer program and its linear relaxation is reduced. A simple branching operation is then performed that eliminates any noninteger values at the output of the linear program solvers. Simulation results demonstrate that this simple technique results in solutions very close to the optimum.
With the widespread deployment of high data rate wireless networks and the improvements in video compression technologies, the popularity of and demand for wireless multimedia transmission have been constantly increasing. In an effort to guarantee the user satisfaction under different channel conditions, a number of crosslayer and multiuser resource allocation strategies have been proposed in the literature (see, e.g.,  and references therein). However, as the paradigm for spectrum access shifts towards that of cognitive radio , new algorithms are required to make the most efficient use of the available resources and provide the highest quality of service (QoS) to the subscribers. In such an environment, an important trait of the video processing subsystem is to be adaptive to the fluctuating bandwidth. Consequently, the recent scalable video coding (SVC) extension of the H.264 standard  is a suitable candidate. In SVC, a scalable bit stream can be viewed as a hierarchy of video layers, consisting of a mandatory base layer and a number of enhancement layers. As higher layer data is successfully received and decoded, the perceived quality of the video is improved. It follows that each SVC sequence would impose a minimum rate constraint, corresponding to the base layer rate, and a maximum rate constraint, corresponding to the transmission of all video layers, on the resource allocation sub-system.
Resource allocation algorithms for scalable video transmission over noncognitive networks have been extensively researched over the last decade [1, 4–6]. Recently, the transmission over cognitive radio (CR) networks has become an area of interest (see, e.g., [7–9] and references therein). However, the algorithms found in the literature cannot be applied to a multiuser OFDM-based CR environment as they either do not consider the interference power limit imposed by primary users or do not consider the transmission of H.264 SVC in a multiuser network. As such, the approaches proposed in the literature will not offer optimum performance for the scenario considered in this paper.
An integer programming formulation for the transmission of SVC video over OFDMA-based CR networks.
A simple near-optimal allocation scheme based on linear programming techniques is presented, with results approaching the optimum.
The paper is structured as follows. In Section 2, the initial problem for subcarrier and bit allocation for SVC transmission in a cognitive environment is presented. In Section 3, we present the techniques to strengthen the problem formulation. Section 4 presents a simple branching technique for obtaining a feasible solution. Simulation results are given and discussed in Section 5, and finally, Section 6 concludes the paper.
2. Resource Allocation Problem Formulation for FGS Video Transmission
where is the set of bits allowed on each subcarrier for example, if the supported modulation formats are BPSK, QPSK, 16-QAM, and 64-QAM; is the indicator variable that is equal to 1 only if a number of bits corresponding the th entry of are transmitted on the th carrier of user ; is the rate requirement for the base layer of user , is the rate required for all enhancement layer data to be transmitted, and ; is the interference power limit imposed on the th subcarrier, is the channel gain between the secondary base station and the th user on the th subcarrier, is the interference channel gain on the th subcarrier, and is the power required to transmit bits on a subcarrier if the channel gain is unity at a given target bit error rate. For -QAM modulation, the value of , at a desired bit error rate of , can be calculated using , where is the noise variance and . Alternately, common lookup tables for various modulation and coding schemes can be employed . For notational brevity, we let in later sections.
The first constraint ensures that all users receive at least the base layer, while the second constraint states that no user is to transmit at a rate higher than the highest enhancement layer rate to avoid inefficient use of the scarce resources. The exclusive use of subcarriers is ensured by the third constraint while the fourth inequality enforces that the received power on any subcarrier at the primary receiver should not exceed the defined limit. Note that for coarse grain scalable (CGS) or medium grain scalable (MGS) video transmission, the first two constraints need to be replaced by a single one as explained in .
The optimal solution of (1) can be obtained using common integer program solvers such as the branch-and-bound technique . However, the complexity involved in solving these problems increases exponentially with the number of variables and constraints, making the direct application of known algorithms inappropriate for dynamic systems such as CR networks. One way of reducing the complexity involved in solving integer programs is to make the convex hull of the problem as close to the ideal, integral convex hull as possible. Methods to achieve this goal are introduced in the next section.
3. Tightening the Formulation
These cuts indicate that if the power needed to transmit using a given modulation scheme on a subcarrier is greater than the power limit of that subcarrier, then the indicator variable, , should be equal to zero. Although the ideal formulation (with an ideal formulation, that is, the extreme points of the convex hull are integral, a linear relaxation of the problem would produce the optimal integer solution) has not been achieved, this refinement yields a much tighter formulation than that given in (1). Simulation results showed that the linear relaxation of this enhanced formulation has a much lower integrality gap, compared to the linear relaxation without the extra constraints. For test scenarios with subcarriers, , and , for each user and and users, respectively, and using the channel model described in Section 5.2 for over 2000 different simulation instances, it was observed that using the simplex algorithm  to solve the linear relaxation of (1) with the extra constraints from the lift-and-project led to an integral solution around 13% of simulation cases. This value contrasts with the case without the extra constraints where, in no instances, was an integral solution obtained. Moreover, these simulations showed that the percentage of nonbinary entries at the output of the simplex with the extra constraints was below 2% of the total number of variables, while this percentage was always above without the extra constraints and reached values of up to 17%. Such conditions would imply that solving the problem using the branch-and-bound method is highly impractical due to the large number of iterations that would be required.
Although the lift-and-project method has provided a better formulation with far less fractional entries at the output of the simplex, it is desired that the output of the allocation algorithm be integral in all cases, while not requiring high computational complexity. On that account, we propose a simple branching operation on the nonintegral values obtained from the linearly relaxed problem. This methodology is presented in the next section.
4. Near-Optimal Allocation Schemes
In the above example, it can be seen that indicator variables pertaining to the second user are all binary, while those corresponding to the first one are not. However, it is observed that fractional entries that are greater than zero adds up to unity and both are at position indexed by . To solve the above problem, we propose a simple algorithm: namely to perform a simple branching operation on only the nonbinary values of following the initial execution of the simplex algorithm. In the example, the proposed technique would replace the fractional entries by the best combination of binary entries. The fractional entries of , , could be replaced by either or , and still produce the same optimal value. Note that values of would violate the third constraint of (1) and is thus not considered. The final allocation vector would thus be , which corresponds to the allocation if the problem is solved using the branch and bound technique.
From a geometric perspective, the above branching operation can be viewed as slightly tilting the polytope so that one integer extreme point is favored over noninteger values. An illustration of the process is given in Figure 2. As stated in the previous section, it was observed after extensive simulations that the number of fractional values after running the simplex algorithm is within 2% of the total number of variables over which optimization is carried out. Thus, replacing the fractional entries at the output of the simplex algorithm with the possible binary values such that the constraints are not violated and choosing the one that yields the largest objective value as being the final solution is an efficient technique. This procedure is illustrated in Algorithm 1, where is the objective function, is the allocation vector output from the simplex program with (4) included in the constraint set. If is the number fractional entries from the simplex algorithm, this algorithm has a complexity of the order of . Given that in the worst case, only 2% of the total number of entries are fractional, for practical system sizes, such a technique results in good running time. It should be noted that although for large systems the fractional entries are not always as elegantly placed as is the case in the simple example considered, and the proposed algorithm can still be employed without suffering too severe performance drop compared to the optimal integral solution as shown in the results section.
Algorithm 1: Branching operation to obtain binary feasible solution.
5.1. Analysis of the Optimality Gap of the Proposed Algorithm
It can be observed that by including the extra constraints derived from the partial projection and performing a simple branching over the linear relaxation of the extended problem, the results obtained are very close to the optimal value. In over 90% of the cases considered, the value reached is around 98% of the optimal, and values above 90% of the optimal value are always achieved. The benefit of this approach is a tremendous gain in processing time and complexity reduction relative to solving the original IP. However, since only the noninteger values are considered in the branching operation without changing other 0–1 variables, as is the case in binary integer programming solvers, the value attained is not always the optimal.
5.2. Performance Analysis of the Near-Optimal Allocation Scheme
5.2.1. Achieved Rate Analysis
The simulations in this section is to demonstrate the effectiveness and performance of our near-optimal resource allocation scheme. We compare the system presented herein to the suboptimal, linear-programming-based rate-adaptive (RA) resource allocation method of [22, 23], where the objective is to maximise the minimum rate in a downlink multiuser OFDM environment, given a total transmit power constraint. This objective is accomplished in a two-stage process. In the first stage, the authors of  assumed that a fixed modulation scheme is employed and each user is assigned a fixed number of subcarriers. Using linear programming techniques, each user is then assigned the required number of subcarriers such that the power required for transmission is minimised. In the second stage, an adaptive bit loading operation is performed such that the rate for each user is increased while not exceeding the total power budget and satisfying the target BER.
5.2.2. PSNR Analysis of Received Video Sequences
In this paper, the resource allocation for scalable video transmission over OFDMA-based cognitive radio networks has been proposed and formulated as an integer program. To reduce the complexity in obtaining the optimal solution, the method of lift-and-project as presented in  is applied to strengthen the problem formulation. This stronger formulation can be solved using linear programming techniques, such as the simplex method, although the solution may occasionally be nonintegral. To obtain an integer feasible solution, we propose a simple branching operation on the fractional values at the output of the simplex method. Simulation results demonstrate that this simple two-step approach leads to a resource allocation that is very close to the optimal. Moreover, it was observed that resource allocation algorithms not considering the interference power constraint could be adapted to cognitive scenarios by scaling down the total transmit power at the expense of a severe performance loss. In contrast, the proposed resource allocation scheme never exceeds the interference power limit, while maximising the sum rate over all users and achieving fairness among multiple transmission. Fairness is ensured by the explicit constraint in the problem formulation that all users should be assigned a rate at least equal to the base layer rate.
The authors would like to thank the directors at Toshiba TRL and the Centre for Communications Research, Bristol, for their continued support.
- Su GM, Han Z, Wu M, Liu KJR: A scalable multiuser framework for video over OFDM networks: fairness and efficiency. IEEE Transactions on Circuits and Systems for Video Technology 2006, 16(10):1217-1231.View ArticleGoogle Scholar
- Mitola J, Maguire GQ: Cognitive radio: making software radios more personal. IEEE Personal Communications 1999, 6(4):13-18. 10.1109/98.788210View ArticleGoogle Scholar
- Schwarz H, Marpe D, Wiegand T: Overview of the scalable video coding extension of the H.264/AVC standard. IEEE Transactions on Circuits and Systems for Video Technology 2007, 17(9):1103-1120.View ArticleGoogle Scholar
- Van der Schaar M, Krishnamachari S, Choi S, Xu X: Adaptive cross-layer protection strategies for robust scalable video transmission over 802.11 WLANs. IEEE Journal on Selected Areas in Communications 2003, 21(10):1752-1763. 10.1109/JSAC.2003.815231View ArticleGoogle Scholar
- Pourmohammadi Fallah Y, Mansour H, Khan S, Nasiopoulos P, Alnuweiri HM: A link adaptation scheme for efficient transmission of H.264 scalable video over multirate WLANs. IEEE Transactions on Circuits and Systems for Video Technology 2008, 18(7):875-887.View ArticleGoogle Scholar
- Juan HH, Huang HC, Huang C, Chiang T: Scalable video streaming over mobile WiMAX. Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '07), May 2007 3463-3466.Google Scholar
- Kushwaha H, Xing Y, Chandramouli R, Heffes H: Reliable multimedia transmission over cognitive radio networks using fountain codes. Proceedings of the IEEE 2008, 96(1):155-165.View ArticleGoogle Scholar
- Shiang HP, Van Der Schaar M: Queuing-based dynamic channel selection for heterogeneous multimedia applications over cognitive radio networks. IEEE Transactions on Multimedia 2008, 10(5):896-909.View ArticleGoogle Scholar
- Mansour H, Huang JW, Krishnamurthy V: Multi-user scalable video transmission control in cognitive radio networks as a Markovian dynamic game. Proceedings of the 48th IEEE Conference held jointly with the 28th Chinese Control Conference Decision and Control (CDC/CCC '09), 2009 4735-4740.Google Scholar
- Radha HM, Van Der Schaar M, Chen Y: The MPEG-4 fine-grained scalable video coding method for multimedia streaming over IP. IEEE Transactions on Multimedia 2001, 3(1):53-68. 10.1109/6046.909594View ArticleGoogle Scholar
- Bao Y, Karczewicz M, Wang X, Ridge J: Fgs coding with adaptive reference for low-delay applications. Proceedings of the IEEE International Image Processing Conference, 2006 185-188.Google Scholar
- Lovász L, Schrijver A: Cones of matrices and set-functions and0–1optimization. SIAM Journal on Optimization 1991, 1: 166-190. 10.1137/0801013MATHMathSciNetView ArticleGoogle Scholar
- Rahulamathavan Y, Cumanan K, Musavian L, Lambotharan S: Optimal subcarrier and bit allocation techniques for cognitive radio networks using integer linear programming. Proceedings of the 15th IEEE/SP Workshop on Statistical Signal Processing (SSP '09), September 2009 293-296.Google Scholar
- Wong CY, Cheng RS, Letaief KB, Murch RD: Multiuser OFDM with adaptive subcarrier, bit, and power allocation. IEEE Journal on Selected Areas in Communications 1999, 17(10):1747-1758. 10.1109/49.793310View ArticleGoogle Scholar
- Jeffrey RM, Andrews G, Ghosh A: Fundamentals of WiMAX: Understanding Broadband Wireless Networking. 1st edition. Prentice Hall, New York, NY, USA; 2007.Google Scholar
- Bocus MZ, Coon JP, Canagarajah CN, McGeehan JP, Armour SMD, Doufexi A: Joint call admission control and resource allocation for H.264 SVC transmission over OFDMA networks. Proceedings of the IEEE 71st Vehicular Technology Conference (VTC '10), 2010 1-5.Google Scholar
- Bertsimas D, Weismantel R: Optimization over Integers. Dynamic Ideas, Belmont, Mass, USA; 2005.Google Scholar
- Lasserre JB: Global optimization with polynomials and the problem of moments. SIAM Journal on Optimization 2001, 11(3):796-817. 10.1137/S1052623400366802MATHMathSciNetView ArticleGoogle Scholar
- Fletcher R: Practical Methods of Optimization. 2nd edition. John Wiley & Sons, New York, NY, USA; 2000.View ArticleGoogle Scholar
- Boyd S, Vandenberghe L: Convex Optimization. Cambridge University Press, Cambridge, UK; 2004.MATHView ArticleGoogle Scholar
- Korte B, Vygen J: Combibatorial Optimization, Theory and Algorithms, Algorithms and Combinatorics. Volume 21. 4th edition. Springer, New York, NY, USA; 2008.Google Scholar
- Kim I, Lee HL, Kim B, Lee YH: On the use of linear programming for dynamic subchannel and bit allocation in multiuser OFDM. Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '01), November 2001 6: 3648-3652.View ArticleGoogle Scholar
- Kim I, Park IS, Lee YH: Use of linear programming for dynamic subcarrier and bit allocation in multiuser OFDM. IEEE Transactions on Vehicular Technology 2006, 55(4):1195-1207. 10.1109/TVT.2006.877490MathSciNetView ArticleGoogle Scholar
- Joint scalable video model jsvm 9.18 JVT of ITU-T VCEG and ISO/IEC MPEGGoogle Scholar
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