Layering as Optimization Decomposition: Questions and Answers

Network protocols in layered architectures have historically been obtained on an ad-hoc basis, and much of the recent cross-layer designs are conducted through piecemeal approaches. Network protocols may instead be holistically analyzed and systematically designed as distributed solutions to some global optimization problems in the form of generalized Network Utility Maximization (NUM), providing insight on what they optimize and on the structures of network protocol stacks. In the form of 10 Questions and Answers, this paper presents a short survey of the recent efforts towards a systematic understanding of "layering" as "optimization decomposition". The overall communication network is modeled by a generalized NUM problem, each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as functions of the optimization variables coordinating the subproblems. Furthermore, there are many alternative decompositions, each leading to a different layering architecture. Industry adoption of this unifying framework has also started. Here we summarize the current status of horizontal decomposition into distributed computation and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, power control, and coding. We also discuss under-explored future research directions in this area. More importantly than proposing any particular crosslayer design, this framework is working towards a mathematical foundation of network architectures and the design process of modularization

Distributed Stochastic Power Control in Ad-hoc Networks: A Nonconvex Case

Utility-based power allocation in wireless ad-hoc networks is inherently nonconvex because of the global coupling induced by the co-channel interference. To tackle this challenge, we first show that the globally optimal point lies on the boundary of the feasible region, which is utilized as a basis to transform the utility maximization problem into an equivalent max-min problem with more structure. By using extended duality theory, penalty multipliers are introduced for penalizing the constraint violations, and the minimum weighted utility maximization problem is then decomposed into subproblems for individual users to devise a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the total utility by simulated annealing. The proposed distributed power control algorithm can guarantee global optimality at the cost of slow convergence due to simulated annealing involved in the global optimization. The geometric cooling scheme and suitable penalty parameters are used to improve the convergence rate. Next, by integrating the stochastic power control approach with the back-pressure algorithm, we develop a joint scheduling and power allocation policy to stabilize the queueing systems. Finally, we generalize the above distributed power control algorithms to multicast communications, and show their global optimality for multicast traffic.Comment: Contains 12 pages, 10 figures, and 2 tables; work submitted to IEEE Transactions on Mobile Computin

Resource Allocation in OFDMA Wireless Networks

Orthogonal frequency division multiple access (OFDMA) is becoming a widely deployed mechanism in broadband wireless networks due to its capability to combat the channel impairments and support high data rate. Besides, dealing with small units of spectrum, named sub-carriers, instead of whole spectrum, results in enhanced flexibility and efficiency of the resource allocation for OFDMA networks. Resource allocation and scheduling in the downlink of OFDMA networks supporting heterogeneous traffic will be considered in this thesis. The purpose of resource allocation is to allocate sub-carriers and power to users to meet their service requirements while maintaining fairness among users and maximizes resource utilization. To achieve these objectives, utility-based resource allocation schemes along with some state-of-the-art resource allocation paradigms such as power control, adaptive modulation and coding, sub-carrier assignment, and scheduling are adopted. On one hand, a utility-based resource allocation scheme improves resource utilization by allocating enough resources based on users' quality of service (QoS) satisfaction. On the other hand, resource allocation based on utilities is not trivial when users demand different traffic types with convex and nonconvex utilities. The first contribution of the thesis is the proposing of a framework, based on joint physical (PHY) and medium access (MAC) layer optimization, for utility-based resource allocation in OFDMA networks with heterogeneous traffic types. The framework considers the network resources limitations while attempting to improve resources utilization and heterogeneous users' satisfaction of service. The resource allocation problem is formulated by continuous optimization techniques, and an algorithm based on interior point and penalty methods is suggested to solve the problem. The numerical results show that the framework is very efficient in treating the nonconvexity problem and the allocation is accurate comparing with the ones obtained by a genetic search algorithm. The second contribution of the thesis is the proposing of an opportunistic fair scheduling scheme for OFDMA networks. The contribution is twofold. First, a vector of fair weights is proposed, which can be used in any scheduling scheme for OFDMA networks to maintain fairness. Second, the fair weights are deployed in an opportunistic scheduling scheme to compensate the unfairness of the scheduling. The proposed scheme efficiently schedules users by exploiting multiuser diversity gain, OFDMA resource allocation flexibility, and utility fair service discipline. It is expected that the research in the thesis contributes to developing practical schemes with low complexity for the MAC layer of OFDMA networks