From Centralized RAN to Open RAN: A Survey on the Evolution of Distributed Antenna Systems

Next-generation mobile networks require evolved radio access network (RAN) architectures to meet the demands of high capacity, massive connectivity, reduced costs, and energy efficiency, and to realize communication with ultra-low latency and ultra-high reliability. {Meeting such} requirements for both mobile users and vertical industries in the next decade {requires novel solutions. One of the potential solutions that attracted significant research attention in the past 15 years} is to redesign the radio access network (RAN). In this survey, we present a comprehensive survey on distributed antenna system (DAS) architectures that address these challenges and improve network performance. We cover the transition from traditional decentralized RAN to DAS, including cloud radio-access networks (C-RAN), fog radio-access networks (F-RAN), virtualized radio-access networks (V-RAN), cell-free massive multiple-input multiple-output (CF-mMIMO), and {the most recent advances manifested in} open radio-access network (O-RAN). In the process, we discuss the benefits and limitations of these architectures, including the impact of limited-capacity fronthaul links, various cooperative uplink and downlink coding strategies, cross-layer optimization, and techniques to optimize the performance of DAS. Moreover, we review key enabling technologies for next-generation RAN systems, such as multi-access edge computing, network function virtualization, software-defined networking, and network slicing; in addition to some crucial radio access technologies, such as millimeter wave, massive multi-input multi-output, device-to-device communication, and massive machine-type communication. Last but not least, we discuss the major research challenges in DAS and identify several possible directions for future research.

Uni-polarized RIS Beamforming for Improving Connectivity of Multi-RIS-Assisted D2D Networks

This paper introduces a novel method to enhance the connectivity of multi-reconfigurable intelligent surface-assisted device-to-device networks, referred to as multi-RIS-assisted D2D networks, through a unique phase shift determination. The proposed method aims to optimize the power-domain array factor (PDAF), targeting specific azimuth angles of reliable user equipments (UEs) and enhancing network connectivity. We formulate an optimization problem that jointly optimizes RIS beamforming design, RIS-aided link selection, and RIS positioning. This problem is a mixed-integer non-binary programming. The optimization problem is divided into two sub-problems, which are solved individually and iteratively. The first sub-problem of RIS-aided link selection is solved using an efficient perturbation method while developing genetic algorithm (GA) to obtain RIS beamforming design. The GA optimizes the RIS phase shift to generate multiple RIS-aided narrowbeams that exhibit significant PDAF towards azimuth angles of interest while minimizing PDAF towards undesired azimuth angles. The second sub-problem of RIS positioning is addressed using the Adam optimizer. Numerical simulations verify the superiority of the proposed scheme in improving network connectivity compared to other schemes, including those utilizing distributed small RISs, each generating one RIS-aided link.

Broad and Spectral-Efficient Beamforming for the Uni-polarized Reconfigurable Intelligent Surfaces

A reconfigurable intelligent surface (RIS) is composed of low-cost elements that manipulate the propagation environment from a transmitter by intelligently applying phase shifts to incoming signals before they are reflected. This paper explores a uni-polarized RIS with linear shape aimed at transmitting a common signal to multiple user equipments (UEs) spread across a wide angular region. To achieve uniform coverage, the uni-polarized RIS is designed to emit a broad and spectral-efficient beam featuring a spatially flat-like array factor, diverging from the conventional narrow beam approach. To achieve this objective, we start by deriving probabilistic lower and upper bounds for the average spectral efficiency (SE) delivered to the UEs. Leveraging the insights from the lower bound, we focus on optimizing the minimum value of the power domain array factor (PDAF) across a range of azimuth angles from \(-\frac{\pi}{2}\) to \(\frac{\pi}{2}\). We employ the continuous genetic algorithm (CGA) for this optimization task, aiming to improve the SE delivered to the UEs while also creating a wide beam. Extensive simulation experiments are carried out to assess the performance of the proposed code, focusing on key metrics such as the minimum and average values of the PDAF and the SE delivered to the UEs. Our findings demonstrate that the proposed code enhances the minimum SE delivered to the UEs while maintaining the desired attribute of a broad beam. This performance is notably superior to that of established codes, including the Barker, Frank, and Chu codes.