In this work, we conceive novel robust hybrid beamformer design schemes for millimeter-wave (mmWave) multi-cell multi-user (MCMU) systems in the presence of channel state information (CSI) uncertainty, that relies on base station (BS) coordination and minimization of total transmit power while ensuring compliance to practical signal-to-interference-noise ratio (SINR) constraints for each user. We consider a scenario where some of the users are located close to the cell boundary and thus desire to receive the signal of interest transmitted by multiple BSs while ensuring the quality-of-service (QoS) constraint. Initially, a Bayesian learning (BL) framework is developed for estimating the sparse mmWave channel of each user in the system. Next, a semidefinite relaxation (SDR) based technique has been proposed for a centralized MCMU system toward designing the fully digital beamformer (FDBF) in the presence of stochastic uncertainty in the estimated channel. Subsequently, a BL technique is employed to split the FDBF into its analog and digital constituents toward obtaining a hybrid transmit precoder (TPC). However, the centralized TPC design requires global CSI, resulting in a high signaling overhead. Next, a distributed coordinated hybrid TPC utilizing the alternating direction method of multipliers (ADMM) algorithm is developed for the mmWave MCMU system in the presence of cell-edge (CE) users. The distributed TPC design solely relies on CSI and only requires a limited exchange of information between the BSs, consequently eliminating the need for the high signaling overheads that come along with the centralized method. Our simulation results illustrate the superior performance of the proposed centralized and distributed robust TPC design methods in comparison to non-coordinated systems.

This paper proposes an integrated sensing and communication (ISAC) framework based on the orthogonal time-frequency space (OTFS) modulation scheme for millimeter wave (mmWave) multiple-input multiple-output (MIMO) dual functional radar and communication (DFRC) systems. Initially, we derive the delay-Doppler (DD)-domain end-to-end input-output model for a phased-array ISAC (PA-ISAC) system, which employs a single RF chain (RFC) both at the DFRC base station (BS) and at the user equipment (UE). Furthermore, a Bayesian learning (BL) based two-stage procedure is developed for transceiver design followed by radar target parameter and channel estimation in PA-ISAC systems, which efficiently exploits the sparsity of the signal scattering environment. Next, the transceiver design, parameter learning, and channel estimation procedures are extended to an OTFS-aided mmWave MIMO ISAC (mM-ISAC) system, wherein multiple RFCs are employed at the DFRC BS and UE, leading to improved sensing and enhanced data rates. Furthermore, it is demonstrated that the radar target parameter and DD-domain channel estimation exhibit block sparsity for mM-ISAC systems. Subsequently, the block-sparse BL (B-BL) scheme is proposed to leverage the block sparsity for improved results. Analytical expressions are derived for the Bayesian Cramér-Rao lower bounds (BCRLB), which serve as benchmarks for the mean squared error (MSE) of both the target parameter and channel estimates. Finally, our simulation results illustrate the superior performance of the proposed transceiver designs, parameter and channel estimation techniques for both PA-ISAC and mM-ISAC systems.

Cooperative hybrid transmit precoder (TP) and receive combiner (RC) design algorithms are conceived for cell-free millimeter wave (mmWave) multiple-input multiple-output (MIMO) networks, operating in the face of asynchronous interference (ASI). To begin with, a Wiener filtering-based optimal hybrid TP/RC (WHB-U) design is proposed for unicast scenarios that minimizes the normalized mean squared error (NMSE) between the received signal and the desired signal subject to user-specific power constraints. Next, a signal-to-leakage plus noise ratio (SLNR) maximization-based hybrid TP/RC design (SHB-U) is conceived, which reduces the interference engendered by the signal transmission targeted towards a specific user, rather than focuses on the interference at a particular user. Next, a multicast transmission scenario is considered, wherein the users belonging to a particular multicast group request identical information. Toward this, the WHB-M and SHB-M hybrid TP/RC schemes are designed for mitigating both the inter-user and inter-group interference. Subsequently, we also develop a Bayesian learning (BL)-based framework for jointly designing the RF and baseband (BB) TPs/RCs for both unicast and multicast scenarios, which does not require the full knowledge of mmWave MIMO channel components. Finally, the efficiency of the proposed TP/RC schemes is extensively evaluated by simulations both in terms of their ability to mitigate the ASI, and the spectral efficiency attained.

Novel hybrid beamformer designs are conceived for a multi-user multi-cell (MUMC) mmWave system relying on base station (BS) coordination and total transmit power minimization subject to realistic signal-to-interference-plus-noise ratio (SINR) constraints at each mobile station (MS). Initially, a semidefinite relaxation (SDR)-based approach is developed for a centralized MUMC system to determine the fully digital beamformer having perfect CSI. Subsequently, a Bayesian learning (BL) technique is harnessed for decomposing the fully-digital (FD) solution into its analog and digital components for constructing a hybrid transceiver. Next, an alternating direction method of multipliers (ADMM) based distributed hybrid beamformer is designed for the same system, which requires only local CSI and limited information exchange among the BSs, thus avoiding the excessive signalling overheads required by the centralized approach. Then we further extend both the centralized and the above distributed hybrid designs to construct robust beamformers that minimize the worst-case transmit power with imperfect CSI. Our robust beamforming techniques leverage the S-lemma, which is eminently suitable for the infinitely many constraints arising from the associated CSI uncertainty. Finally, our simulation results demonstrate the improved performance of the proposed centralized and distributed methods over the system having no coordination.