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Analog antenna combining architectures have recently received increased interest due to their reduced size and power consumption, in comparison with the conventional multiple-input–multiple-output (MIMO) approach that operates in the baseband. In this paper, we design algorithms for three new radio-frequency (RF) architectures that are based on different combinations of phase shifters and variable gain amplifiers. From a baseband point of view, each architecture poses a different beamforming design problem in which the transmit/receive beamformers are constrained to have constant-modulus complex, real, or nonnegative real weights, respectively. Assuming perfect channel knowledge, we consider the problem of designing these constrained RF beamformers under orthogonal frequency-division-multiplexing (OFDM) transmissions. For the general MIMO case, the resulting optimization problems are not convex and are, therefore, difficult to solve. However, the single-input–multiple-output (SIMO) and multiple-input–single-output cases either have a closed-form solution or can be reformulated as convex problems. Exploiting this fact, an alternating optimization procedure is used to find a suboptimal solution for the MIMO case. The performance of the different RF-MIMO architectures is compared by means of Monte Carlo simulations, which allows us to conclude that the architecture based solely on phase shifters (RF equal-gain beamforming) provides the best results. [ABSTRACT FROM PUBLISHER]

Cellular location-based services need to use a variety of positioning methods to meet requirements on availability, response time, and accuracy. Due to the geometrical properties of the different positioning methods, the number of standardized reporting formats is large. Because the user requirements on the reporting formats are as scattered as the geometrical properties of the positioning methods, there is a need for shape conversion algorithms that transform between different formats. This paper discusses a case that transforms between a polygon and an ellipse while preserving the confidence between the two shapes. The transformation is required for emergency positioning outside North America, and when fingerprinting, localization is used in the Global System for Mobile Communications (GSM). The algorithm has a number of technically interesting properties, e.g., with regard to the selection of the optimization criterion and the fact that the solution is computed by an analytical solution of a cubic equation. [ABSTRACT FROM PUBLISHER]

In this paper, we study joint performance optimization on network lifetime and video distortion for an energy-constrained wireless video sensor network (WVSN). To seek an appropriate tradeoff between maximum network lifetime and minimum video distortion, a framework for joint source/channel rate adaptation is proposed, where the video encoding rate, link rate, and power consumption are jointly considered, formulating a weighted convex optimization problem. In the context of lossy wireless channels, an efficient error control scheme that couples network coding and multipath routing is explored. Moreover, an integrated power consumption model, including power dissipation on video compression, error control, and data communication, is specifically developed for the video sensor node. By primal decomposition, the original problem is decomposed into a two-level optimization procedure, with the high-level procedure for source adaptation (source rate optimization) and the low-level procedure for channel adaptation (network resource allocation). Finally, a fully decentralized iterative algorithm is developed to resolve the target optimization problem. Extensive simulation results evaluate the convergence performance of the proposed algorithm and demonstrate the best tradeoff performance. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) for dynamic spectrum sensing and access has been a hot research topic in recent years. To avoid collision with the primary users, secondary users need to sense the channels before transmitting on them, which is referred to as sensing time overhead. Our previous work shows that the spectral correlations between the channels within the same service are sufficiently high for accurate prediction, which can further be used to reduce the sensing time. With such motivation, in this paper, we propose a new definition, i.e., channel availability vector (CAV), to characterize the state information of a group of licensed channels by introducing spectrum prediction while focusing on the scenario of a single secondary user with multiple channels and leverage it by formulating the throughput optimization problem as a Markov decision process, which is further solved by our optimal sensing scheme and verified with the real spectrum measurement data. The results show that our prediction-based sensing scheme outperforms one existing work. [ABSTRACT FROM PUBLISHER]

Worldwide interoperability for microwave access (WiMAX) is a promising technology for last-mile Internet access, particularly in the areas where wired infrastructures are not available. In a WiMAX network, call admission control (CAC) is deployed to effectively control different traffic loads and prevent the network from being overloaded. In this paper, we propose a framework of a 2-D CAC to accommodate various features of WiMAX networks. Specifically, we decompose the 2-D uplink and downlink WiMAX CAC problem into two independent 1-D CAC problems and formulate the 1-D CAC optimization, in which the demands of service providers and subscribers are jointly taken into account. To solve the optimization problem, we develop a utility- and fairness-constrained optimal revenue policy, as well as its corresponding approximation algorithm. Simulation results are presented to demonstrate the effectiveness of the proposed WiMAX CAC approach. [ABSTRACT FROM AUTHOR]

Energy constraints have a significant impact on the design and operation of wireless sensor networks. This paper investigates the base station (BS) selection (or anycast) problem in wireless sensor networks. A wireless sensor network having multiple BSs (data sink nodes) is considered. Each source node must send all its locally generated data to only one of the BSs. To maximize network lifetime, it is essential to optimally match each source node to a particular BS and find an optimal routing solution. A polynomial time heuristic is proposed for optimal BS selection and anycast via a sequential fixing procedure. Through extensive simulation results, it is shown that this algorithm has excellent performance behavior and provides a near-optimal solution. [ABSTRACT FROM AUTHOR]