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Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, this paper investigates the problem of path selection and rate allocation for multi-hop self-backhaul millimeter-wave (mm-wave) networks. Enabling multi-hop mm-wave transmissions raises a potential issue of increased latency, and thus, this paper aims at addressing the fundamental questions: how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints? In this regard, a new system design, which exploits multiple antenna diversity, mm-wave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast to as a network utility maximization, subject to the upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: 1) path selection and 2) rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a non-convex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, the comprehensive performance analysis and convergence proof are provided for the proposed solution. The numerical results show that the proposed approach ensures reliable communication with a guaranteed probability of up to 99.9999% and reduces latency by 50.64% and 92.9% as compared to baseline models. Furthermore, the results showcase the key tradeoff between latency and network arrival rate. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the problem of providing gigabit wireless access with reliable communication in 5G millimeter-wave (mmWave) massive multiple-input multiple-output networks. In contrast to the classical network design based on average metrics, we propose a distributed risk-sensitive reinforcement learning-based framework to jointly optimize the beamwidth and transmit power, while taking into account the sensitivity of mmWave links due to blockage. Numerical results show that our proposed algorithm achieves more than 9 Gbps of user throughput with a guaranteed probability of 90%, whereas the baselines guarantee less than 7.5 Gbps. More importantly, there exists a rate-reliability-network density tradeoff, in which as the user density increases from 16 to 96 per km2, the fraction of users that achieves 4 Gbps is reduced by 11.61% and 39.11% in the proposed and the baseline models, respectively. [ABSTRACT FROM PUBLISHER]

Ultra-reliability and low latency are two key components in 5G networks. In this letter, we investigate the problem of ultra-reliable and low-latency communication in millimeter wave-enabled massive multiple-input multiple-output networks. The problem is cast as a network utility maximization subject to probabilistic latency and reliability constraints. To solve this problem, we resort to the Lyapunov technique, whereby a utility-delay control approach is proposed, which adapts to channel variations and queue dynamics. Numerical results demonstrate that our proposed approach ensures reliable communication with a guaranteed probability of 99.99%, and reduces latency by 28.41% and 77.11% as compared to baselines with and without probabilistic latency constraints, respectively. [ABSTRACT FROM PUBLISHER]

We study the problem of joint load balancing and interference mitigation in heterogeneous networks in which massive multiple-input multiple-output macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs), is assumed. Self-backhauled SC BSs with full-duplex communication employing regular antenna arrays serve both macro users and SC users by using the wireless backhaul from macro BS in the same frequency band. We formulate the joint load balancing and interference mitigation problem as a network utility maximization subject to wireless backhaul constraints. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users, backhaul provisioning of SCs, and offloading macro cell users to SCs as a function of interference and backhaul links. Via numerical results, we show the performance gains of our proposed framework under the impact of SCs density, number of BS antennas, and transmit power levels at low and high frequency bands. It is shown that our proposed approach achieves a 5.6 times gain in terms of cell-edge performance as compared with the closed-access baseline in ultra-dense networks with 350 SC BSs per ${{\text{km}}}^{2}$ . [ABSTRACT FROM PUBLISHER]

In the last couple of years, the increasing penetration of renewable energy resulted in the development of grid-connected large-scale power plants. However, a high penetration harbors the risk of grid instability if the generating power plants are not able to support the grid. Therefore, grid stabilization, which depends on the system-type or grid of each country, plays an important role and has been strengthened by different grid codes. With this background, VDE-AR-N 4105 for photovoltaic (PV) systems connected to the low-voltage grid and the German Association of Energy and Water Industries (BDEW) introduced the medium-voltage grid code for connecting power plants to the grid and they are the most stringent certifications. In this paper, the control strategy of generating system is enhanced with VDE-AR-N 4105 and BDEW grid code, where both active/reactive powers are controlled. Simulation and experimental results of 100-kW PV inverter are shown to verify the effectiveness of the proposed implemental control strategy. [ABSTRACT FROM AUTHOR]