Joint power and admission control via p norm minimization deflation

In an interference network, joint power and admission control aims to support a maximum number of links at their specified signal to interference plus noise ratio (SINR) targets while using a minimum total transmission power. In our previous work, we formulated the joint control problem as a sparse $\ell_0$-minimization problem and relaxed it to a $\ell_1$-minimization problem. In this work, we propose to approximate the $\ell_0$-optimization problem to a p norm minimization problem where $0<p<1$, since intuitively p norm will approximate 0 norm better than 1 norm. We first show that the $\ell_p$-minimization problem is strongly NP-hard and then derive a reformulation of it such that the well developed interior-point algorithms can be applied to solve it. The solution to the $\ell_p$-minimization problem can efficiently guide the link's removals (deflation). Numerical simulations show the proposed heuristic outperforms the existing algorithms.Comment: 2013 IEEE International Conference on Acoustics, Speech, and Signal Processin

Joint Bandwidth and Power Allocation with Admission Control in Wireless Multi-User Networks With and Without Relaying

Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Joint bandwidth and power allocation strategies for wireless multi-user networks with and without relaying are proposed in this paper for (i) the maximization of the sum capacity of all users; (ii) the maximization of the worst user capacity; and (iii) the minimization of the total power consumption of all users. It is shown that the proposed allocation problems are convex and, therefore, can be solved efficiently. Moreover, the admission control based joint bandwidth and power allocation is considered. A suboptimal greedy search algorithm is developed to solve the admission control problem efficiently. The conditions under which the greedy search is optimal are derived and shown to be mild. The performance improvements offered by the proposed joint bandwidth and power allocation are demonstrated by simulations. The advantages of the suboptimal greedy search algorithm for admission control are also shown.Comment: 30 pages, 5 figures, submitted to IEEE Trans. Signal Processing in June 201

Sample Approximation-Based Deflation Approaches for Chance SINR Constrained Joint Power and Admission Control

Consider the joint power and admission control (JPAC) problem for a multi-user single-input single-output (SISO) interference channel. Most existing works on JPAC assume the perfect instantaneous channel state information (CSI). In this paper, we consider the JPAC problem with the imperfect CSI, that is, we assume that only the channel distribution information (CDI) is available. We formulate the JPAC problem into a chance (probabilistic) constrained program, where each link's SINR outage probability is enforced to be less than or equal to a specified tolerance. To circumvent the computational difficulty of the chance SINR constraints, we propose to use the sample (scenario) approximation scheme to convert them into finitely many simple linear constraints. Furthermore, we reformulate the sample approximation of the chance SINR constrained JPAC problem as a composite group sparse minimization problem and then approximate it by a second-order cone program (SOCP). The solution of the SOCP approximation can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure (the deflation approach). We exploit the special structure of the SOCP approximation and custom-design an efficient algorithm for solving it. Finally, we illustrate the effectiveness and efficiency of the proposed sample approximation-based deflation approaches by simulations.Comment: The paper has been accepted for publication in IEEE Transactions on Wireless Communication

Gradual Removals in Cellular PCS with Constrained Power Control and Noise

this paper we study the mobile removal problem in a cellular PCS network where transmitter powers are constrained and controlled by a Distributed Constrained Power Control (DCPC) algorithm. Receivers are subject to non-negligible noise, and the DCPC attempts to bring each receiver&apos;s CIR above a given target. To evaluate feasibility and computational complexity, we assume a paradigm where radio bandwidth is scarce and inter-base station connection is fast. We show that finding the optimal removal set is an NP-Complete problem, giving rise for heuristic algorithms. We study and compare among three classes of transmitter removal algorithms. Two classes consist of algorithms which are invoked only when reaching a stable power vector under DCPC. The third class consist of algorithms which combine transmitter removals with power control. These are One-by-one Removals, Multiple Removals, and Power Control with Removals Combined. In the class of power control with removals combined, we also consider a distributed algorithm which uses the same local information as DCPC does. All removal algorithms are compared with respect to their outage probabilities and their time to converge to a stable state. Comparisons are made in a hexagonal macro-cellular system, and in two metropolitan micro-cellular systems. Th

Transmitter Optimization in Multiuser Wireless Systems with Quality of Service Constraints

In this dissertation, transmitter adaptation for optimal resource allocation in wireless communication systems are investigated. First, a multiple access channel model is considered where many transmitters communicate with a single receiver. This scenario is a basic component of a. wireless network in which multiple users simultaneously access the resources of a wireless service provider. Adaptive algorithms for transmitter optimization to meet Quality-of-Service (QoS) requirements in a distributed manner are studied. Second, an interference channel model is considered where multiple interfering transmitter-receiver pairs co-exist such that a given transmitter communicates with its intended receiver in the presence of interference from other transmitters. This scenario models a wireless network in which several wireless service providers share the spectrum to offer their services by using dynamic spectrum access and cognitive radio (CR) technologies. The primary objective of dynamic spectrum access in the CR approach is to enable use of the frequency band dynamically and opportunistically without creating harmful interference to licensed incumbent users. Specifically, CR users are envisioned to be able to provide high bandwidth and efficient utilization of the spectrum via dynamic spectrum access in heterogeneous networks. In this scenario, a distributed method is investigated for combined precoder and power adaptation of CR transmitters for dynamic spectrum sharing in cognitive radio systems. Finally, the effect of limited feedback for transmitter optimization is analyzed where precoder adaptation uses the quantized version of interference information or the predictive vector quantization for incremental updates. The performance of the transmitter adaptation algorithms is also studied in the context of fading channels

Optimal resource scheduling for energy-efficient next generation wireless networks

Cellular networks can provide highly available and reliable communication links to the Internet of Things (IoT) applications, letting the connected Things paradigm gain much more momentum than ever. Also, the rich information collected from the Things with sensing capabilities can guide the network operator to an unforeseen direction, allowing the underlying cellular networks to be further optimized. In this regard, the cellular networks and IoT are conceived as the key components of the beyond-4G and future 5G networks. Therefore, in this dissertation, we study each of the two components in depth, focusing on how to optimize the networking resources for the quality service and better energy-efficiency. To begin with, we study the heterogeneous cellular network architecture which is a major enhancement to the current 4G network by means of the base station (BS) densification and traffic offloading. In particular, the densely deployed short-range, low-power smallcell base stations (SBSs) can significantly improve the frequency reuse, throughput performance and the energy-efficiency. We then study the heterogeneous C-RAN (cloud radio access network), which is one of the core enablers of the next generation 5G cellular networks. In particular, with the high availability provided by the long-range macro BS (MBS), the heterogeneous C-RAN (H-CRAN) can effectively enhance the overall resource utilization compared to the conventional C-RANs. In each study, we propose an optimal resource scheduling and service provisioning scheme to provide a quality service to users in a resource-efficient manner. In addition, we carry out two studies for the Internet of Things (IoT) networks operating with the IEEE 802.11ah standard. Specifically, we introduce energy-efficient device management algorithms for the battery-operated, resource-constrained IoT sensor devices to prolong their lifetime by optimally scheduling their activation. The enhanced power saving mechanism and the optimal sensing algorithm that we propose in each study can effectively improve both the energy-efficiency of the IoT devices and the lifetime of the entire network