With the emerging technology for distributed generation and urge of improving quality of service of power supply for energy users, more and more Microgrids (MGs) are integrated into the distributed networks to serve the energy users. These Microgrids are gradually formulating a Multi-Microgrid System (MMGS, Multi-Microgrid System), which will play an important role for the future energy supply. Building a centralized control center not only increases the expense of investment, but also brings the issues of maintaining fairness among energy users. To address these problems, this paper proposes a peer-to-peer method for energy trading of MMGS, based on the idea of decentralized trading. An auction-based trading mechanism suitable for peer-to-peer energy trading is proposed first. For this mechanism, the MGs firstly declare their energy buying bids or selling quotations. Then, the market-clearing price is determined by using a unified weight clearing algorithm in a decentralized manner. By applying the edge technology of Blockchain, the implementation of the proposed peer-to-peer energy trading method, including the architecture, procedures, security check, etc., is also discussed. The proposed Blockchain-based energy trading platform can realize the decentralized and autonomous energy trading of MGs within an MMGS. A case study with an MMGS with 10 MG units is provided to demonstrate the effectiveness of the proposed approach.

Distributed precoding is a meaningful topic in the cell-free massive multiple input multiple output system. This system faces challenges in performance degradation due to the absence of knowledge from other antennas and several realistic constraints brought by the distributed implementation of the communication system such as the presence of phase noise (PN). In this paper, a robust cascaded team minimum mean square error (RCT-MMSE) precoding based on a hierarchical fronthaul structure is exploited to handle distributed and robust precoding including not only PN but signaling noise, sharing cost constraints and channel aging uncertainty. Such RCT-MMSE precoding, characterized by its avoidance of iterations because we derive the analytic expressions, mitigates the need for high fronthaul level instantaneous information exchange. It also demonstrates scalability with distributed computation burden and flexible signaling overhead, which offers an advantageous performance-cost tradeoff. Simulation results demonstrate the effectiveness of RCT-MMSE to combat several practical constraints and provide a flexible distributed precoding framework compared with previous ones.

This paper investigates the distributed uplink for the hierarchically backhaul-linked cell-free network with distributed processors to maximize the advantages of jointly serving under the same time and frequency resources. It is validated in previous works that the performance of fully centralized uplink in a cell-free network overwhelms uplink methods without or with limited coordination. On the other hand, a fully centralized uplink requires extremely high costs on backhaul links and computation capacity on the central processing unit (CPU), which is impractical in a widely deployed large cell-free network. To handle the mentioned problems, the relation between centralized uplink and group sliced distributed uplink is revealed, firstly. With the uniform framework compatible with previous fully centralized and fully distributed minimal mean square error (MMSE) equalization, two theorems are derived as group-joint MMSE complementary and the column space equivalence, which indicate the relation between the local optimal and the global optimal and can include conclusions achieved in previous works. Both computation and backhaul signaling overheads are distributed among the whole network. Simulation results demonstrate the excellent performance of proposed methods based on the derived complementary kernel.

Hybrid precoding is used in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems instead of full digital precoding to reduce the cost and power consumption. Conventional hybrid precoding however requires complex channel estimation before beamforming. In this paper, we propose a low complexity low-cost channel estimation-free deep direct beamforming method composed of two basic steps-beam impulse and beamforming for time division duplex (TDD). This method is built upon randomized singular value decomposition (RSVD) and can directly complete the process of beamforming. Furthermore, a realistic dataset of mmWave channel-DeepMIMO is adopted to evaluate and determine the practicability of this method. The simulation results demonstrate that the proposed deep direct beamforming achieves high performance while its complexity and power consumption are very low. Compared with other methods without perfect channel status information (CSI), the proposed method costs less and requires lower computational resources while achieves higher spectral efficiency. Furthermore, the RSVD can be easily combined with conventional beamforming methods hence the proposed method is easily extendable to other applications.