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.
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.
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