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Recent years have witnessed a proliferation of battery-powered mobile devices, e.g., smartphones, tablets, sensors, and laptops, which leads a significant demand for high capacity wireless communication with high energy efficiency. Among technologies to provide the efficiency is full-duplex wireless communication. Full-duplex wireless enhances capacity by simultaneously transmitting uplink and downlink data with limited frequency resources. Previous studies on full-duplex wireless mostly focuses on doubling the network capacity, whereas in this paper we discuss that full-duplex wireless can also provide higher energy efficiency. We propose low power communication by wireless full-duplexing (LPFD), focusing on the fact that the full-duplex communication duration becomes half of the half-duplex communication duration. In the LPFD, by using the sleep state in which the transceiver provided in the wireless communication terminal is turned off, power consumption of the wireless communication terminal is reduced and energy efficiency in wireless full duplex is improved. Simulation results show that the energy efficiency achieved by LPFD is up to approximately 17.3 times higher than the energy efficiency achieved by existing full-duplex medium access protocol. Further, it is up to approximately 27.5 times higher than the energy efficiency using power saving mode of half-duplex communication.

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We consider joint subchannel and power allocation for an orthogonal frequency division multiple access (OFDMA)based cognitive radio network. We formulate the downlink resource allocation problem as a spectral-footprint (bandwidthpower product) minimization problem under interference threshold at primary users, total power and quality of service constraints. The cognitive base station solves this non-convex mixed-integer programming problem iteratively by dividing it into a subchannel allocation master problem and power allocation subproblems. The subchannel assignment problem is solved by applying a modified Hungarian algorithm while the power allocation subproblems are solved by using Lagrangian techniques. Specifically, we propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. The performance of our spectral-footprint minimization technique is compared with the waterfilling power allocation.

This paper evaluates the performance of adaptive modulation in multi-relay networks with selective relaying, under Nakagami-m fading. In the system model, the source decides independently whether to forward the source message to the destination via the best (partial relay selection) relay path or direct path by comparing the end-to-end instantaneous signalto-noise ratio (SNR) at the destination, which is independent of the modulation scheme. Adaptive discrete-rate M -ary quadrature amplitude modulation with fixed switching thresholds is implemented by dividing the SNR region into five modes. In particular, impact of imperfect (outdated) channel estimation due to feedback delay is quantified for relay selection. We derive the cumulative distribution function for the upper-bound of endto-end SNR in closed-form. Further, lower-bounds of outage probability and average bit error rate, and upper-bound of spectral efficiency are derived in closed-forms. Monte Carlo simulation results validate our numerical analysis.