This paper proposes a unified framework for the effective rate analysis over arbitrary correlated and not necessarily identical multiple inputs single output (MISO) fading channels, which uses moment generating function (MGF) based approach and H transform representation. The proposed framework has the potential to simplify the cumbersome analysis procedure compared to the probability density function (PDF) based approach. Moreover, the effective rates over two specific fading scenarios are investigated, namely independent but not necessarily identical distributed (i.n.i.d.) MISO hyper Fox's H fading channels and arbitrary correlated generalized K fading channels. The exact analytical representations for these two scenarios are also presented. By substituting corresponding parameters, the effective rates in various practical fading scenarios, such as Rayleigh, Nakagami-m, Weibull/Gamma and generalized
Phasor Measurement Units (PMUs) have enabled real-time power grid monitoring and control applications realizing an integrated power grid and communication system. The communication network formed by PMUs has strict latency requirements. If PMU measurements cannot reach the control centre within the latency bound, they will be invalid for calculation and may compromise the observability of the whole power grid as well as related applications. To address this issue, this study proposes a model to account for the power grid observability under communication constraints, where effective capacity is adopted to perform a cross-layer statistical analysis in the communication system. Based on this model, three algorithms are proposed for improving power grid observability, which are an observability redundancy algorithm, an observability sensitivity algorithm and an observability probability algorithm. These three algorithms aim at enhancing the power system observability via the optimal communication resource allocation for a given grid infrastructure. Case studies show that the proposed algorithms can improve the power system performance under constrained wireless communication resources. scalability [11]. Hence, wireless communication is playing a more and more important role in supporting the communication needs of modern grid [12]. In IEEE Standard 2030.2-2015 [13], the application of wireless technology for the communication between components within a transmission network and the operation control centre has been identified. There have been various researches addressing the wireless communication network in supporting communication between PMUs [14-17] as well as components of SCADA system [18-20]. Yet wireless communication is broadcasting in nature, which makes propagation signal prone to the influence of physical environment. The effect of channel fading will induce communication system performance fluctuation and then result in communication delay. However, the communication delay's influence on the power system observability performance as well as the inter-discipline study of the power system and communication system has not been well addressed, which is the major focus of this paper. Communication latency is a link layer metric used in the Open Systems Interconnection (OSI) model. In practical systems, communication delay has many sources. Some latencies are fixed or bounded, such as system overheads. Others are time-varying and hard, if not impossible, to be bound. One major uncertainty contributed to this time-varying latency is due to the communication channel fading effect. However, typically latency is a metric considered in link layer but not physical layer, where the latency study is further complicated when the channel has parameters that change with time. Therefore, it requires sophisticated cross-layer analysis to study such problems. Another challenge is that, in most fading channel scenarios, it is not feasible to provide a deterministic bound for the communication delay, which is a consequence ...
This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-inputsingle-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes.Index Terms-Deep learning, beamforming, MISO, SINR balancing, per-antenna power constraints. ).We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan Xp GPU used for this research.
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