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This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE International Conference on Information Networking. , 2012-02-01

{In this paper, we consider a finite geographic area with multiple mobile stations (MSs) uniformly distributed within the area and multiple candidate locations (CLs) for deploying base stations (BSs) and relay stations (RSs) to serve the MSs. For this network scenario, we study the joint optimal placement of BSs and RSs into those CLs and MS and RS power allocations such that the sum-capacity of the network is maximized while the target data rate of each MS is achieved. In order to investigate the energy-efficiency trade-offs between deploying BSs and RSs, we provide an iterative algorithm which first maximizes the sum-rate of the network by optimally deploying a certain number of BSs. Then, the algorithm decreases the number of BSs to be deployed optimally by one and continues deploying RSs until the same sum-rate is achieved. The process continues until the number of optimally deployed BS is 1 and the number of optimally deployed RSs is less than or equal to the total number of candidate RS locations. Our numerical results suggest that significant gains in terms of reduction of total transmitted power can be obtained by replacing BSs with RSs. However, this gain diminishes when the number of BSs became too small which makes the BS-RS and RS-MS distances too large for energy efficient communications.}

{Measurement studies have shown that uneven and dynamic usage patterns by the primary users of license based wireless communication systems often lead to temporal and spatial spectrum underutilization. This provides an opportunity for secondary users (SU) to tap into underutilized frequency bands provided that they are capable of cognitively accessing the networks without colliding or impacting the performance of the primary users (PU). When there are multiple networks with spare spectrum, secondary users can opportunistically choose the best network to access, subject to certain constraints. In cognitive radio systems, this is referred to as the network selection problem for secondary users. This paper first formulate a Markov queuing model to obtain the maximum allowable arrival rate of secondary users subject to a target collision probability for the primary users. Based on this model, a Collision-Constrained Network Selection (CCNS) method is proposed to maximize secondary users throughput subject to a given PU collision probability. Two more approaches, referred as CCNS-Greedy and CCNS-Energy, are designed to furthermore reduce collision probability and to decrease energy consumption of secondary users, when the system is underloaded. However, CCNS strictly relies on PU and SU traffic characteristics such as inter-arrival time and service time. Then, MEAsurement-based Networks Selection (MEANS) is proposed to perform network selection for secondary users based on online measurement of PU collision probability of each network, with the same objective to regulate PU collision probability of each network below the target value. Later, an enhanced MEANS (MEANS+) is designed to improve SU throughput while not violating the target PU collision probability. Simulation based extensive performance evaluation has shown that the proposed schemes achieve the best performance in terms of resulted PU collision probability, SU throughput, and SU energy consumption, compared to Random and Greedy strategies.}

The definitive version was published in Mobile VCE Green Radio, Software Defined Radio - Wincomm. , 2011-06-23, http://europe.wirelessinnovation.org/mc/page/MobileVCE

{We consider a wireless relay network (WRN) where multiple mobile stations (MSs) try to send their data to a base station (BS) either directly or via a set of fixed relay stations (RSs). For this network, we study the problem of joint optimal MS and RS power allocation and relay selection with the objective of minimizing the total transmitted power of the system. The joint optimization algorithm must satisfy the minimum data demand of each MS. We formulate the problem as a mixed integer nonlinear programming (MINLP) problem and find the solution under different relaying architectures and spatial diversity schemes. The optimal solution of the MINLP problem is exponentially complex due to its combinatorial nature. We use the MATLAB based commercial software TOMLAB to find a near optimal solution of the MINLP problem. We also find an approximate solution of the original problem by applying a simple relay selection scheme based on the channel gains between MSs and RSs. Numerical results are presented to show the performance of this simple scheme with respect to the optimal one in terms of total power consumption.}