Distributed Channel Assignment and Precoder Design for Multiantenna Dynamic Spectrum Access Systems

We consider a cognitive radio (CR) network in which nodes employ multi-input multi-output (MIMO) for spatial multiplexing (realized through precoding matrices). Our objective is to jointly allocate available channels to various CR links such that no channel is allocated to more than one link, and simultaneously optimize the precoding matrices (one per allocated channel) at each transmitter so as to ensure fairness or maximize network throughput. For the fairness objective, we formulate the problem based on Nash bargaining (NB). The proposed bargaining problem is combinatorial with mixed variables. Even if we relax its integer variables, the problem is still non-convex. To tackle it, we first convexify the relaxed version and provide a timesharing interpretation of the new problem. Using dual decomposition, we develop an optimal distributed algorithm for this timesharing problem, which sheds light on how to design a distributed algorithm for the original bargaining problem. Our distributed algorithm allows CR users to propose their requested rates, negotiate their channel assignment, and configure their precoding matrices. Following the same approach, we also consider the network throughput maximization problem (NET-MAX). Using convexification and dual decomposition, a distributed algorithm for this problem is developed. Simulations confirm the convergence of our distributed algorithms to the globally optimal solutions of both the NB-based and NET-MAX problems. It is shown that the NB-based algorithm achieves much better fairness with moderate throughput reduction, compared with the NET-MAX algorithm. Our algorithms are applicable not only to MIMO CR ad hoc networks, but also to cellular MIMO/SISO networks (on the uplink) and OFDMA-based systems. Index Terms Cooperative games, Nash bargaining, fairness, dual decomposition, distributed algorithm, throughput maximization, cognitive radio, OFDMA, MIMO, MAC protocol, power allocation, frequency assignment.

[1]  Baochun Li,et al.  Efficient Resource Allocation with Flexible Channel Cooperation in OFDMA Cognitive Radio Networks , 2010, 2010 Proceedings IEEE INFOCOM.

[2]  Jie Chen,et al.  Applying Bargaining Solutions to Resource Allocation in Multiuser MIMO-OFDMA Broadcast Systems , 2012, IEEE Journal of Selected Topics in Signal Processing.

[3]  Georgios B. Giannakis,et al.  Optimal resource allocation for MIMO ad hoc cognitive radio networks , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[4]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[5]  Zhi-Quan Luo,et al.  Dynamic Spectrum Management: Complexity and Duality , 2008, IEEE Journal of Selected Topics in Signal Processing.

[6]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[7]  J. Nash THE BARGAINING PROBLEM , 1950, Classics in Game Theory.

[8]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[9]  Shuguang Cui,et al.  Price-Based Spectrum Management in Cognitive Radio Networks , 2007, IEEE Journal of Selected Topics in Signal Processing.

[10]  Daniel Pérez Palomar,et al.  Practical algorithms for a family of waterfilling solutions , 2005, IEEE Transactions on Signal Processing.

[11]  Anthony Man-Cho So,et al.  Optimal Spectrum Sharing in MIMO Cognitive Radio Networks via Semidefinite Programming , 2011, IEEE Journal on Selected Areas in Communications.

[12]  Qiang Ni,et al.  Nash Bargaining Game Theoretic Scheduling for Joint Channel and Power Allocation in Cognitive Radio Systems , 2012, IEEE Journal on Selected Areas in Communications.

[13]  Geert Leus,et al.  Joint Dynamic Resource Allocation and Waveform Adaptation for Cognitive Networks , 2011, IEEE Journal on Selected Areas in Communications.

[14]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[15]  Sergio Barbarossa,et al.  IEEE TRANSACTIONS ON SIGNAL PROCESSING (ACCEPTED) 1 The MIMO Iterative Waterfilling Algorithm , 2022 .

[16]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[17]  Daniel Pérez Palomar,et al.  Power Control By Geometric Programming , 2007, IEEE Transactions on Wireless Communications.

[18]  Marwan Krunz,et al.  Spectrum management and power allocation in MIMO cognitive networks , 2012, 2012 Proceedings IEEE INFOCOM.

[19]  John M. Cioffi,et al.  Increase in capacity of multiuser OFDM system using dynamic subchannel allocation , 2000, VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026).

[20]  Ying-Chang Liang,et al.  Exploiting Multi-Antennas for Opportunistic Spectrum Sharing in Cognitive Radio Networks , 2007, IEEE Journal of Selected Topics in Signal Processing.

[21]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[22]  Zhu Han,et al.  Fair multiuser channel allocation for OFDMA networks using Nash bargaining solutions and coalitions , 2005, IEEE Transactions on Communications.