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This paper addresses the energy minimization problem in Device-to-Device (D2D) assisted Mobile Edge Computing (MEC) networks under the latency constraint of each individual task and the computing resource constraint of each computing entity. The energy minimization problem is formed as a twostage optimization problem. Specifically, in the first stage, an initial feasibility problem is formed to maximize the number of executed tasks and the global energy minimization problem is tackled in the second stage while maintaining the maximum number of executed tasks. Both of the optimization problems in two stages are NP-hard, therefore a low-complexity algorithm is developed for the initial feasibility problem with a supplementary algorithm further proposed for energy minimization. Simulation results demonstrate the near-optimal performance of the proposed algorithms and the fact that with the assistance of D2D communication, the number of executed tasks is greatly increased and the energy consumption per executed task is significantly reduced in MEC networks, especially in dense user scenario.
Moving to millimeter wave (mmWave) frequencies and deploying massive multiple input multiple output (MIMO) antenna arrays have shown great potential of supporting highdata-rate communications in the fifth-generation (5G) and beyond wireless networks, thanks to the availability of huge amounts of mmWave frequency bandwidth and massive numbers of narrow and high gain beams. A number of massive MIMO beamforming techniques have been proposed, among which the fixed-beam scheme has attracted considerable interests from both academia and industry due to its simplicity and requirement of a small number of radio frequency (RF) chains compared to the number of base-station (BS) antennas. Moreover, a beam allocation based pure analog fixed-beam system requires much lower complexity and less signalling overhead than the hybrid beamforming based fixed-beam system, which can therefore be easily implemented in the practical systems. In this paper, the sum data rate of beam allocation based multiuser massive MIMO systems is studied where a near-optimal low complexity beam allocation algorithm is adopted. Simulation results show that our derived average sum data rate serves as a good approximation of the simulation results.
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