This paper proposes a multi-agent Q-learning-based resource allocation algorithm that allows long-term evolution (LTE)-enabled device-to-device (D2D) communication agents to generate the orthogonal transmission schedules outside the network coverage. This algorithm reduces packet drop rates (PDR) in distributed D2D communication networks to meet the quality-of-service requirements of the microgrid communications. The data traffic characteristics of three archetypal smart grid applications, namely demand response, solar, and generation forecasting, and synchrophasor communications, were simulated under seven different traffic congestion scenarios, where the total aggregate throughput of users ranged from 50% to 140% channel utilization. The PDR and latency performance of the proposed algorithm were compared with the existing random self-allocation mechanism introduced under the Third-Generation Partnership Project's LTE Release 12 standard for such scenarios. Our algorithm outperformed the LTE algorithm for all tested scenarios, demonstrating 20%-40% absolute reductions in PDR and 10-20-ms reductions in latency for all microgrid applications. The use of our algorithm in a simulated D2D-enabled demand response application resulted in a hundredfold reduction in power oscillations about the desired power flows.
In this paper, we address inter-beam inter-cell interference mitigation in 5G networks that employ millimeterwave (mmWave), beamforming and non-orthogonal multiple access (NOMA) techniques. Those techniques play a key role in improving network capacity and spectral efficiency by multiplexing users on both spatial and power domains. In addition, the coverage area of multiple beams from different cells can intersect, allowing more flexibility in user-cell association. However, the intersection of coverage areas also implies increased inter-beam inter-cell interference, i.e. interference among beams formed by nearby cells. Therefore, joint user-cell association and interbeam power allocation stand as a promising solution to mitigate inter-beam, inter-cell interference. In this paper, we consider a 5G mmWave network and propose a reinforcement learning algorithm to perform joint user-cell association and inter-beam power allocation to maximize the sum rate of the network. The proposed algorithm is compared to a uniform power allocation that equally divides power among beams per cell. Simulation results present a performance enhancement of 13 − 30% in network's sum-rate corresponding to the lowest and highest traffic loads, respectively.
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