Massive MISO Research Articles

UB3: Fixed Budget Best Beam Identification in mmWave Massive MISO via Pure Exploration Unimodal Bandits

UB3: Fixed Budget Best Beam Identification in mmWave Massive MISO via Pure Exploration Unimodal Bandits

Asymptotic Analysis of Near-Field Coupling in Massive MISO and Massive SIMO Systems

Asymptotic Analysis of Near-Field Coupling in Massive MISO and Massive SIMO Systems

A power method based user selection for reconfigurable intelligent surface (RIS) assisted multiuser massive MISO systems

Reconfigurable intelligent surfaces (RISs) have recently emerged as effective solutions for providing massive connectivity. However, an effective RIS user selection method that can support massive devices remains undeveloped. In this study, a power method (PM)-based RIS user selection algorithm for RIS-assisted massive multiple-input multiple-output systems is proposed. It is proven that minimizing the condition number (CN) of the aggregated downlink channel matrix results in the same performance as maximizing the sum rate. Based on this analysis, a PM-based RIS user selection algorithm that utilizes a CN as an RIS user selection metric is proposed here. Although the complexity of conventional user selection algorithm is proportional to the square of the number of base station (BS) antennas ( M 2 ), that of the proposed algorithm is linearly proportional to the number of BS antennas ( M ). Simulation results demonstrate that, under a practical RIS control link, the proposed PMUS algorithm achieves better performance and lower complexity than the conventional user selection algorithm.

Analysis and Optimization of Spectral and Energy Efficiency in Underlaid D2D Multi-Cell Massive MISO Over Rician Fading

This paper investigates the uplink spectral efficiency (SE) characterization and energy efficiency (EE) optimization of device-to-device (D2D) communications underlying a multi-cell massive multiple-input single-output (m-MISO) network, assuming that the channels are modeled with Rician fading. First, an analytical expression for the lower-bound of the ergodic capacity of a typical cellular user (CU) is derived with imperfect channel state information in the presence of D2D links’ interference. Then, a closed-form approximate achievable SE expression for a typical D2D pair is derived considering Rician fading between D2D pairs. The asymptotic SE behavior is analyzed in strong line-of-sight (LOS) conditions for CU and D2D pairs. Also, simplified expressions are obtained for the special case of Rayleigh fading. Next, to optimize the total EE, a transmit data power allocation based on the derived rate expressions is formulated. Since the optimization problem has a non-linear and non-convex objective function, it is intractable to be solved straightforwardly. Therefore, we resort to utilizing an iterative algorithm relying on fractional programming. The numerical results show that a stronger LOS component (i.e., larger Rician K-factor) between a typical CU and its serving BS leads to a significant improvement in the system performance, while it has less effect on the D2D network.

An Efficient Beamforming Architecture to Handle the Trade-Off Between Performance and Hardware Complexity in Multiuser Massive MISO Systems

Beamforming is the fundamental concept of wireless communications to serve several users through multiple-antenna transceivers. The advent of massive multiple-input multiple-output (MIMO) systems leads to an investigation into beamforming to reach optimal performance. However, fully-digital beamforming implementation has to face important challenges in terms of consumed power and cost of the RF chains and converters. To solve the problem, some studies focus on reducing hardware complexity by dividing the beamforming into digital and analog parts, known as hybrid beamforming (HBF). In HBF systems, the dominant part of the hardware complexity depends on the architecture of the analog network, which determines the required RF components. In this paper, we categorize the analog beamforming parts based on the connectivity between RF-chains and antennas. Moreover, we show the impacts of the connection on the analog beamforming matrix. To this aim, we propose an efficient connectivity architecture in which the antennas are divided into fully-connected and singly-connected groups so that we can deal with the hardware complexity-performance trade-off. In addition, to achieve further performance improvements, we propose a dynamic architecture that adjusts the connection of the RF-chain antenna to the channel state information (CSI) through a switch network. Analytically, we propose an efficient approach to calculate the zero-forcing precoder, which is proper for both fixed and dynamic architectures. A two-part algorithm based on the greedy search method has also been developed to obtain switch states in dynamic architecture and then implemented by a deep neural network (DNN). The simulation results confirm the theoretical analysis and the suitability of the proposed architecture.

Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems

Channel estimation in the RIS-aided massive multiuser multiple-input single-output (MU-MISO) wireless communication systems is challenging due to the passive feature of RIS and the large number of reflecting elements that incur high channel estimation overhead. To address this issue, we propose a novel cascaded channel estimation strategy with low pilot overhead by exploiting the sparsity and the correlation of multiuser cascaded channels in millimeter-wave massive MISO systems. Based on the fact that the phsical positions of the BS, the RIS and users may not change in several or even tens of consecutive channel coherence blocks, we first estimate the full channel state information (CSI) including all the angle and gain information in the first coherence block, and then only re-estimate the channel gains in the remaining coherence blocks with much less pilot overhead. In the first coherence block, we propose a two-phase channel estimation method, in which the cascaded channel of one typical user is estimated in Phase I based on the linear correlation among cascaded paths, while the cascaded channels of other users are estimated in Phase II by utilizing the partial CSI of the common base station (BS)-RIS channel obtained in Phase I. The total theoretical minimum pilot overhead in the first coherence block is $8J-2+(K-1)\left\lceil (8J-2)/L\right\rceil $, where $K$, $L$ and $J$ denote the numbers of users, paths in the BS-RIS channel and paths in the RIS-user channel, respectively. In each of the remaining coherence blocks, the minimum pilot overhead is $JK$. Moreover, the training phase shift matrices at the RIS are optimized to improve the estimation performance.

TDD Massive MISO Piloting Strategy With User Information: A Reinforcement Learning Approach

In TDD massive MISO systems, user equipments (UEs) send channel measurement pilots to the BS for beamforming. Frequently sending these pilots, although improving beamforming, could consume significant communication resources. In this letter, we investigate how frequent these pilots should be sent for each UE so as to increase overall throughput performance for TDD massive MISO downlink. This real-time resource allocation problem is challenging due to non-trivial performance metric and boundary conditions. Assuming that instantaneous speed and location information of UEs can be obtained by the BS, we propose a reinforcement learning framework in which the BS acts as a learning agent to decide pilot intervals. Simulation results show that, for this multi-terminal setting where UEs compete for resources, using this centralized reinforcement learning framework, performance can be improved by choosing pilot intervals and transmission rates based on the UE information.

다중 사용자 대용량 MISO 시스템에서 전력 전송을 위한 하이브리드 프리코더 설계 기법

다중 사용자 대용량 MISO 시스템에서 전력 전송을 위한 하이브리드 프리코더 설계 기법