Distributed phased arrays based multiple-input multiple-output (DPA-MIMO) is a newly introduced architecture that enables both spatial multiplexing and beamforming while facilitating highly reconfigurable hardware implementation in millimeter-wave (mmWave) frequency bands. With a DPA-MIMO system, we focus on channel state information (CSI) acquisition and hybrid precoding. As benefited from a coordinated and open-loop pilot beam pattern design, all the sub-arrays can perform channel sounding with less training overhead compared with the traditional orthogonal operation of each sub-array. Furthermore, two sparse channel recovery algorithms, known as joint orthogonal matching pursuit (JOMP) and joint sparse Bayesian learning with $\ell_2$ reweighting (JSBL- $\ell_2$ ), are proposed to exploit the hidden structured sparsity in the beam-domain channel vector. Finally, successive interference cancellation (SIC) based hybrid precoding through sub-array grouping is illustrated for the DPA-MIMO system, which decomposes the joint sub-array RF beamformer design into an interactive per-sub-array-group handle. Simulation results show that the proposed two channel estimators fully take advantage of the partial coupling characteristic of DPA-MIMO channels to perform channel recovery, and the proposed hybrid precoding algorithm is suitable for such array-of-sub-arrays architecture with satisfactory performance and low complexity.

Confronted with the challenges of interruptions and blockages caused by dense obstacles in millimeter-wave (mmWave) communication, we propose to employ a flexible distributed reconfigurable intelligent surface (RIS) assisted massive multiple-input multiple-output (MIMO) system to improve performance in areas with poor coverage. In a scenario featuring multi-antenna user equipments (UEs), we consider the practical additive hardware impairments (AHIs) at transceivers and conduct a comprehensive analysis of their impact on the system performance. Leveraging the pronounced beam directivity inherent in mmWave MIMO, we design phase shifts of RISs and analog beamformers of transceivers to achieve beam alignment. In light of this, we explore a linear minimum mean-square error (LMMSE) equivalent channel estimation method. Furthermore, we derive the closed-form expressions for downlink achievable spectral efficiency (SE) in the presence of AHIs, utilizing statistical channel state information (CSI) and maximum ratio transmission (MRT). Based on the derived closed-form expressions, we propose an efficient power allocation strategy relying on an intelligent algorithm known as primal-dual optimization based deep deterministic policy gradient (PDO-DDPG), which can ensure safe exploration of the agent. Numerical results confirm the accuracy of the derived closed-form expressions, unveil the impact of AHIs on the achievable SE, and verify the effectiveness of the PDO-DDPG based power allocation.

This paper investigates a long-term average total energy cost minimization problem via resource management, including admission control, power allocation, and Energy Sharing (ES) of renewable energy in Heterogeneous Cellular Networks powered by Grid-connected Smart Micro Grids (GSMG-HCNs). In GSMG-HCNs, both renewable and grid energy power the base stations. Unlike existing works, we consider the cost of both renewable and grid energy and formulate the power line loss process caused by ES into our model. To solve the proposed problem, we transform it into a real-time issue by the Lyapunov technique. The proposed Cost-Aware Online Resource Management (CAORM) algorithm decouples the real-time issue into two sub-problems, one of which is linear and the other is addressed based on the successive convex approximation approach. We theoretically prove the asymptotic optimality of the CAORM algorithm and a tradeoff between the average total energy cost and the average queue length. Simulation results reveal that the CAORM algorithm outperforms benchmarks in reducing total energy cost and can make appropriate decisions according to different unit costs of renewable energy. Besides, the designed distance-related ES loss rate can help obtain better solutions with lower ES losses.

Vehicular edge computing is an emerging enabler in strengthening driving efficiency and traffic safety. However, both performance bottlenecks and low resource efficiency of heterogeneous edge servers arise concurrently because of the inhomogeneous load distribution among the servers. Further, the unsaturated infrastructure coverage situation can deteriorate the concurrent issues. Although transmitting raw task data with large sizes among heterogeneous edge servers can relieve the concurrent issues, it distinctly degrades the core network’s efficiency, especially during rush hours. Meanwhile, it cannot settle the unsaturated coverage situation. To relieve the concurrent issues without degrading the core network’s efficiency, we introduce an aerial relay station that can flexibly relay vehicular tasks to nearby heterogeneous edge servers. The long-term task scheduling problem without any prior environment knowledge for the considered vehicular edge system is crucial but still up in the air. We formulate the system latency minimization problem as a partially observable stochastic game. Then a model-free multi-agent reinforcement learning algorithm is developed to search the real-time load-aware scheduling policy. Besides, we design a practical factor named offloading latency gain to assist the training process of the learning algorithm. Simulation experiments show that our proposed algorithm can better exploit idle computation resources of heterogeneous edge infrastructures and significantly reduce the average system latency up to 15-20% over existing algorithms.

In complex indoor environments, there is often no Line of Sight (LOS) path between the transmitter and the receiver due to walls, moving people, and many other obstacles. Traditional localization method based on LOS measurement is no longer available which provides lower localization accuracy in Non-Line of Sight (NLOS) environments. In response to this problem, this paper proposes a joint localization algorithm for scatterers and target user based on indoor prior information in NLOS scenarios. The algorithm makes use of the prior localization information of the access point (AP) and the wall to determine the position of the scatterers and target user simultaneously. Since the established constrained optimization problem is non-convex which is a difference of convex (DC) problem, it is transformed into a convex one based on a Taylor series expansion (TSE) method before it is solved. At the same time, as only a single AP signal can be received normally, the proposed method is then extended to the scenario with only one AP. Simulation and real-world testing results both show that not only the proposed method based on multiple APs, but also the method based on a single AP achieve high positioning accuracy, which are better than the existing positioning methods mentioned in this article as well.

High energy costs and carbon-neutral targets emphasize the economics and sustainability of mobile communications. This paper studies a long-term average energy transaction expenditure minimization problem for smart-grid powered heterogeneous cellular networks (SG-HCNs) where renewable energy is introduced. Renewable energy and wireless channel dynamics evolve over different timescales. Thus, we seek a two-timescale dynamic resource management solution in SG-HCNs, where the real-time joint issue of flow control, power allocation, and energy sharing of renewable energy, and the ahead-of-time two-way energy trading are considered. Based on a two-layer Lyapunov framework, a two-timescale dynamic optimization (TTDO) algorithm is developed for the proposed problem. Specifically, the real-time joint issue is decoupled into two subproblems, addressed by linear programming and successive convex approximation methods. An approximate solution for ahead-of-time two-way energy trading is achieved via the stochastic subgradient approach, where past data of related random events is referred to as prior knowledge that is required but difficult to acquire. Theoretically, the proposed TTDO algorithm can attain an asymptotic optimum and ensure queue stability. Simulation results verify the theoretical analysis and reveal that the proposed TTDO algorithm can obtain lower energy transaction expenditure than benchmarks. Besides, the proposed TTDO algorithm presents an energy-saving property.