P. Chathuranga Weeraddana, PhD Thesis
Abstract
The application of optimization techniques for resource management in wireless communication networks is considered in this thesis. It is understood that a wide variety of resource management problems of recent interest, including power/rate control, link scheduling, crosslayer control, network utility maximization, beamformer design of multipleinput multipleoutput networks, and many others are directly or indirectly reliant on the general weighted sumrate maximization (WSRMax) problem. Thus, in this dissertation a greater emphasis is placed on the WSRMax problem, which is known to be NPhard.
A general method, based on the branch and bound technique, is developed, which solves globally the nonconvex WSRMax problem with an optimality certificate. Efficient analytic bounding techniques are derived as well. More broadly, the proposed method is not restricted to WSRMax. It can also be used to maximize any system performance metric, which is Lipschitz continuous and increasing on signaltointerference plusnoise ratio. The method can be used to find the optimum performance of any network design method, which relies on WSRMax, and therefore it is also useful for evaluating the performance loss encountered by any heuristic algorithm. The considered linkinterference model is general enough to accommodate a wide range of network topologies with various node capabilities, such as singlepacket transmission, multipacket transmission, simultaneous transmission and reception, and many others.
Since global methods become slow in largescale problems, fast local optimization methods for the WSRMax problem are also developed. First, a general multicommodity, multichannel wireless multihop network where all receivers perform singleuser detection is considered. Algorithms based on homotopy methods and complementary geometric programming are developed for WSRMax. They are able to exploit efficiently the available multichannel diversity. The proposed algorithm, based on homotopy methods, handles efficiently the self interference problem that arises when a node transmits and receives simultaneously in the same frequency band. This is very important, since the use of supplementary combinatorial constraints to prevent simultaneous transmissions and receptions of any node is circumvented. In addition, the algorithm together with the considered interference model, provide a mechanism for evaluating the gains when the network nodes employ self interference cancelation techniques with different degrees of accuracy. Next, a similar multicommodity wireless multihop network is considered, but all receivers perform multiuser detection. Solutions for the WSRMax problem are obtained by imposing additional constraints, such as that only one node can transmit to others at a time or that only one node can receive from others at a time. The WSRMax problem of downlink OFDMA systems is also considered. A fast algorithm based on primal decomposition techniques is developed to jointly optimize the multiuser subcarrier assignment and power allocation to maximize the weighted sumrate (WSR). Numerical results show that the proposed algorithm converges faster than Lagrange relaxation based methods.
Finally, a distributed algorithm for WSRMax is derived in multipleinput singleoutput multicell downlink systems. The proposed method is based on classical primal decomposition methods and subgradient methods. It does not rely on zero forcing beamforming or high signaltointerferenceplusnoise ratio approximation like many other distributed variants. The algorithm essentially involves coordinating many local subproblems (one for each base station) to resolve the intercell interference such that the WSR is maximized. The numerical results show that significant gains can be achieved by only a small amount of message passing between the coordinating base stations, though the global optimality of the solution cannot be guaranteed.
Keywords
distributed optimization methods, global (nonconvex) optimization methods, mathematical optimization, radio resource management, weighted sumrate maximization
Download
Thesis: Optimization Techniques for Radio Resource Management in Wireless Communication Networks
Bibliography: Bib
