We consider a cellular massive MIMO system where swarms of wireless repeaters are deployed to improve coverage. These repeaters are full-duplex relays with small form factors that receive and instantaneously retransmit signals. They can be deployed in a plug-and-play manner at low cost, while being transparent to the network—conceptually they areactive channel scattererswith amplification capabilities. Two fundamental questions need to be addressed in repeater deployments: (i) How can we prevent destructive effects of positive feedback caused by inter-repeater interaction (i.e., each repeater receives and amplifies signals from others)? (ii) How much performance improvement can be achieved given that repeaters also inject noise and may introduce more interference? To answer these questions, we first derive a generalized Nyquist stability criterion for the repeater swarm system, and provide an easy-to-check stability condition. Then, we study the uplink performance and develop an efficient iterative algorithm that jointly optimizes the repeater gains, user transmit powers, and receive combining weights to maximize the weighted sum rate while ensuring system stability. Numerical results corroborate our theoretical findings and show that the repeaters can significantly improve the system performance, both in sub-6 GHz and millimeter-wave bands. The results also warrant careful deployment to fully realize the benefits of repeaters, for example, by ensuring a high probability of line-of-sight links between repeaters and the base station.

This paper studies integrated sensing and communication (ISAC) with dynamic time division duplex (DTDD) cell-free (CF) massive multiple-input multiple-output (mMIMO) systems. DTDD enables the CF mMIMO system to concurrently serve both uplink (UL) and downlink (DL) users with spatially separatedhalf-duplex (HD)access points (APs) using the same time-frequency resources. Further, to facilitate ISAC, the UL APs are utilized for both UL data and target echo reception, while the DL APs jointly transmit the precoded DL data streams and target signal. In this context, we present centralized and distributed generalized likelihood-ratio tests (GLRTs) for target detection treating UL users’ signals as sensing interference. We then quantify the optimality and complexity trade-off between distributed and centralized GLRTs and benchmark the respective estimators with the Bayesian Cramér-Rao lower bound for target radar-cross section (RCS). Then, we present a unified framework for joint UL users’ data detection and RCS estimation. Next, for communication, we derive the signal-to-noise-plus-interference (SINR) optimal combiner accounting for the cross-link and radar interference for UL data processing. In DL, we use regularized zero-forcing for the users and propose two types of precoders for the target: one “user-centric” that nullifies the interference caused by the target signal to the DL users and one “target-centric” based on the dominant eigenvector of the composite channel between the target and the APs. Finally, numerical studies corroborate with our theoretical findings and reveal that theGLRT is robust to inter-AP interference, and DTDD doubles the 90%-likely sum UL-DL SE compared to traditional TDD-based CF-mMIMO ISAC systems; while using HD hardware.

This paper presents a framework for channel estimation and beamformer design to jam adversarial drones using a monostatic massive multiple-input multiple-output (MIMO) base station (BS) architecture for the legitimate party. We adopt an iterative least-square (ILS) method to estimate the channels between the adversarial drones and the BS, as well as the radar cross-sections (RCSs) associated with the drones. Unlike existing literature, the developed algorithm is generic (i.e., agnostic to the underlying channel model) and can be translated to a parametric model as a special case. Furthermore, we propose a jamming beamforming optimization problem that aims to maximize the minimum jamming signal strength. The effectiveness of the proposed schemes is demonstrated through numerical simulations, which indicate that jamming performance close to perfect channel state information can be achieved with moderate to high values of RCS variance.

This paper studies integrated sensing and communication (ISAC) within the framework of dynamic time division duplex (DTDD) cell-free (CF) massive multiple-input multiple-output (mMIMO) systems. DTDD enables the CF-mMIMO system to cater to both uplink (UL) and downlink (DL) users with spatially separated half-duplex (HD) access points (APs) using the same time-frequency resources. Thus, in our work, the same set of UL APs is utilized for both UL data and target echo reception, while the DL APs transmit jointly precoded DL data streams and the target symbol. In UL, we present a generalized likelihood-ratio test (GLRT) at the central processing unit (CPU) for target detection and use signal-to-noise-plus-interference (SINR) optimal combiner for UL data processing. In DL, we propose two types of precoder: one “communication-centric” that nullifies the interference caused by the target signal to the DL users; and one “target-centric” based on the dominant eigenvector of the composite channel between the target and the APs. Finally, we numerically investigate the performance of the GLRT and also sum UL-DL spectral efficiency (SE) of the communication users and benchmark the results with conventional TDD-based systems. We observe the GLRT is robust to inter-AP interference, and DTDD almost doubles the 90%-likely sum UL-DL SE compared to traditional TDD-based CF-mMIMO ISAC systems.

The benefits of cell-free multiple-input multiple-output (CF MIMO) systems over traditional cellular systems, viz., a dramatic improvement of spectral efficiency (SE) and uniform quality of service, crucially depend on the quality of the estimated channels at the access points (APs). However, in a CF MIMO system, where a large number of user-equipments (UEs) are served by distributed APs, ensuring pilot contamination-free channel estimates across all the APs requires inordinately high pilot length, which substantially reduces the time available for data transmission. This paper proposes a novel pilot design and allocation algorithm that ensures no pilot contamination among any pair of UEs that are proximal to a common AP, and this is guaranteed at all APs. Further, our algorithm procures the pilot allocation with a minimum number of orthogonal pilots being reused across the UEs. Specifically, we recast the problem as a graph-vertex coloring problem and solve it via a low-complexity algorithm known to be optimal for all bipartite graphs. Unlike existing solutions, our algorithm does not require additional signaling overhead, e.g., signal-to-interference plus noise ratio exchanges, for pilot assignment. Numerical results illustrate the superiority of the proposed technique over existing methods from the literature.

In this paper, we present a comparative study of half-duplex (HD) access points (APs) with dynamic time-division duplex (DTDD) and full-duplex (FD) APs in cell-free (CF) systems. Although both DTDD and FD CF systems support concurrent downlink (DL) transmission and uplink (UL) reception capability, the sum spectral efficiency (SE) is limited by various cross-link interferences. We first present a novel pilot allocation scheme that minimizes the pilot length required to ensure no pilot contamination among the user equipments (UEs) served by at least one common AP. Then, we derive the sum SE in closed form, considering zero-forcing combining and precoding along with the signal-to-interference plus noise ratio optimal weighting at the central processing unit. We also present a provably convergent algorithm for joint UL-DL power allocation and UL/DL mode scheduling of the APs (for DTDD) to maximize the sum SE. Further, the proposed algorithms are precoder and combiner agnostic and come with closed-form update equations for the UL and DL power control coefficients. Our numerical results illustrate the superiority of the proposed pilot allocation and power control algorithms over several benchmark schemes and show that the sum SE with DTDD can outperform an FD CF system with similar antenna density. Thus, DTDD combined with CF is a promising alternative to FD that attains the same performance using HD APs, while obviating the burden of intra-AP interference cancellation.

We consider a dynamic time division duplex (DTDD) enabled cell-free massive multiple-input multiple-output (CF-mMIMO) system, where each half-duplex (HD) access point (AP) is scheduled to operate in the uplink (UL) or downlink (DL) mode based on the data demands of the user equipments (UEs), with the goal of maximizing the sum UL-DL spectral efficiency (SE). We develop a new, low complexity, greedy algorithm for the combinatorial AP scheduling problem, with an optimality guarantee theoretically established via showing that a lower bound of the sum UL-DL SE is sub-modular. We also consider pilot sequence reuse among the UEs to limit the channel estimation overhead. In CF systems, all the APs estimate the channel from every UE, making pilot allocation problem different from the cellular case. We develop a novel algorithm that iteratively minimizes the maximum pilot contamination across the UEs. We compare the performance of our solutions, both theoretically and via simulations, against a full duplex (FD) multi-cell mMIMO system. Our results show that, due to the joint processing of the signals at the central processing unit, CF-mMIMO with dynamic HD AP-scheduling significantly outperforms cellular FD-mMIMO in terms of the sum SE and 90% likely SE. Thus, DTDD enabled HD CF-mMIMO is a promising alternative to cellular FD-mMIMO, without the cost of hardware for self-interference suppression.