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.

We propose a novel resource-efficient over-the-air (OTA) computation framework to address the huge fronthaul computational and control overhead requirements in cell-free massive multiple-input multiple-output (MIMO) networks. We show that the global sufficient statistics to decode the data symbols can be computed OTA using the locally available information at the access points (APs). We provide the essential signal processing aspects at the APs and the central processing unit (CPU) to facilitate the OTA computation of sufficient statistics. The proposed framework scales effectively with an increase in the number of APs. We also make a comprehensive study of the benefits of an OTA framework compared to a conventional digital fronthaul in terms of the overhead associated in transferring the sufficient statistics from the APs to the CPU. To evaluate the performance of the OTA framework, we give closed-form expressions for the mean-square error (MSE) of the estimators of sufficient statistics and the overall data estimator. Furthermore, we assess the symbol error rate (SER) and bit error rate (BER) of the user equipment (UEs) data to demonstrate the efficacy of our method, and benchmark them against the state-of-the-art wired fronthaul networks.

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.

We propose a novel resource efficient analog over-the-air (OTA) computation framework to address the demanding requirements of the uplink (UL) fronthaul between the access points (APs) and the central processing unit (CPU) in cell-free massive multiple-input multiple-output (MIMO) systems. We discuss the drawbacks of the wired and wireless fronthaul solutions, and show that our proposed mechanism is efficient and scalable as the number of APs increases. We present the transmit precoding and two-phase power assignment strategies at the APs to coherently combine the signals OTA in a spectrally efficient manner. We derive the statistics of the APs' locally available signals which enable us to to obtain the analytical expressions for the Bayesian and classical estimators of the OTA combined signals. We empirically evaluate the normalized mean square error (NMSE), symbol error rate (SER), and the coded bit error rate (BER) of our developed solution and benchmark against the state-of-the-art wired fronthaul based system.

We consider a robust beamforming problem where large amount of downlink (DL) channel state information (CSI) data available at a multiple antenna access point (AP) is used to improve the link quality to a user equipment (UE) for beyond-5G and 6G applications such as environment-specific initial access (IA) or wireless power transfer (WPT). As the DL CSI available at the current instant may be imperfect or outdated, we propose a novel scheme which utilizes the (unknown) correlation between the antenna domain and physical domain to localize the possible future UE positions from the historical CSI database. Then, we develop a codebook design procedure to maximize the minimum sum beamforming gain to that localized CSI neighborhood. We also incorporate a UE specific parameter to enlarge the neighborhood to robustify the link further. We adopt an indoor channel model to demonstrate the performance of our solution, and benchmark against a usually optimal (but now sub-optimal due to outdated CSI) maximum ratio transmission (MRT) and a subspace based method. We numerically show that our algorithm outperforms the other methods by a large margin. This shows that customized environment-specific solutions are important to solve many future wireless applications, and we have paved the way to develop further data-driven approaches.

We consider the problem of joint channel estimation and data decoding in uplink massive multiple input multiple output systems with low resolution analog-to-digital converters (ADCs) at the base station. The nonlinearities introduced by the ADCs make the problem challenging: in particular, the existing linear detectors perform poorly. Also, the channel coding used in commercial wireless systems necessitates soft symbol detection to obtain satisfactory performance. In this paper, we present a low-complexity variational Bayesian (VB) inference procedure to jointly solve the (possibly correlated) channel estimation and soft symbol decoding problem. We present the approach in progressively more complex scenarios, including the case where even the channel statistics are not available at the receiver. Finally, we combine our proposed VB procedure with a belief propagation (BP) based channel decoder, which further enhances the performance without any additional complexity. We numerically evaluate the bit error rate (BER) and the normalized mean squared error (NMSE) in the channel estimates obtained by our algorithm as a function of various system parameters, and benchmark the performance against genie-aided and state-of-the-art receivers. The results show that VB procedure is a promising technique for the design of low-complexity advanced receivers in low resolution ADC based systems.

Disjunct matrices play a central role in non-adaptive group testing, as they provide necessary and sufficient conditions for identifying defective items from a large population using a small number of tests. In this paper, we show that binary disjunct matrices can also be very useful for recovering sparse signals from underdetermined linear measurements. They admit non-iterative, ultra-low complexity recovery of sparse signals. Binary measurement matrices have the added benefit of being friendly for hardware implementation. Further, we generalize the notion of disjunctness to matrices with arbitrary (non-binary) entries and show that such matrices also admit similar fast sparse vector recovery algorithms. We empirically demonstrate that disjunct matrices can recover denser signals than recent non-iterative sparse recovery algorithms.

In this paper, we study the problem of downlink (DL) sum rate maximization in codebook based multiuser (MU) multiple input multiple output (MIMO) systems. The user equipments (UEs) estimate the DL channels using pilot symbols sent by the access point (AP) and feedback the estimates to the AP over a control channel. We present a closed form expression for the achievable sum rate of the MU-MIMO broadcast system with codebook constrained precoding based on the estimated channels, where multiple data streams are simultaneously transmitted to all users. Next, we present novel, computationally efficient, minorization-maximization (MM) based algorithms to determine the selection of beamforming vectors and power allocation to each beam that maximizes the achievable sum rate. Our solution involves multiple uses of MM in a nested fashion. Based on this approach, we propose and contrast two algorithms, which we call the square-root-MM (SMM) and inverse-MM (IMM) algorithms. The algorithms are iterative and converge to a locally optimal beamforming vector selection and power allocation solution from any initialization. We evaluate the performance and complexity of the algorithms for various values of the system parameters, compare them with existing solutions, and provide further insights into how they can be used in system design.