Meesam Jafri

New delay and Doppler (DD)-domain channel state information (CSI) estimation schemes are conceived for multi-user (MU)-orthogonal time frequency space (OTFS) multiple-input multiple-output (MIMO) systems, for both uplink (UL) and downlink (DL)-scenarios. An important feature of the proposed schemes is that the pilots are modulated and transmitted directly in the time-domain, leading to a low pilot-overhead, flexible training duration, and reduced complexity. These techniques, therefore, achieve a high bandwidth efficiency, since they eliminate the need for DD-domain guard bands corresponding to all the transmit antennas. Moreover, the DD-domain CSI of the MU-UL system is shown to be row-sparse in nature, while that of the MU-DL scenario exhibits block-sparsity. The multiple measurement vector (MMV)-based Bayesian learning (BL) technique for UL MIMO OTFS (MB-ULMO) is then proposed for exploiting the row-sparsity and for enhancing the UL channel estimation performance. Similarly, for the downlink, the block BL (BB) for DL MIMO OTFS (BB-DLMO) algorithm is proposed in order to exploit the block-sparsity for CSI recovery. Furthermore, the BCRLBs are derived for characterizing the MSEs of the proposed DD-domain channel estimation schemes. A low-complexity linear MMSE detector (LCLMD) is derived for data detection that is ideally suited for practical implementation since it does not require any prior information about the number of active multipath components at the receiver. Our simulation results demonstrate the improved performance of the proposed BL-based schemes both in comparison to extensions of other sparse schemes and to conventional estimation techniques.

A low-complexity robust cooperative hybrid beamformer is designed for both the downlink and uplink of cell-free millimeter wave (mmWave) multiple-input-multiple-output (MIMO) networks, while considering realistic imperfect channel state information (CSI). To begin with, a second-order cone program (SOCP)-based robust fully-digital beamformer (FDBF) is designed for minimizing the worst-case interference for the downlink of multiple-input-single-output (MISO) systems. Subsequently, we develop a Bayesian learning (BL) framework for determining both the analog and digital components of the hybrid transmit precoder (TPC) from the FDBF. The above designs are subsequently extended to employing eigenvector perturbation theory for constructing the robust TPC for the cell-free mmWave MIMO downlink, where the users have multiple receive antennas (RAs). Furthermore, the multi-dimensional covariance fitting (MCF) framework is harnessed for designing the robust TPC of the corresponding uplink. Finally, the efficiency of the proposed TPC designs is evaluated by simulation results both in terms of their ability to mitigate the multi-user interference (MUI), and improving the spectral efficiency achieved. Additionally, the proposed designs are shown to be computationally efficient and equivalent to a minimum variance hybrid beamformer.

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.

A new affine-precoded superimposed pilot (AP-SIP) scheme is conceived for both wireless channel and radar target parameter estimation in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems. The AP-SIP scheme leads to enhanced estimation accuracy and improved utilization of spectral resources. Initially, the pilot-assisted radar (PAR) and data-assisted radar (DAR) parameter estimation models are separately developed for the estimation of the radar target parameters. Subsequently, these are combined into a joint pilot-data radar (JPDR) model for simultaneously harnessing both the signals to further boost the estimation accuracy. The sparse Bayesian learning (BL)-based joint-BL (J-BL) technique is developed for this system that efficiently exploits the sparsity of the radar scattering environment. Next, a group sparse BL (G-BL) technique is also derived that exploits the group sparsity across subcarriers for the estimation of the wireless beamspace channel vector, which outperforms the competing techniques, including conventional sparse BL. The optimal pilot, transmit precoder (TPC) and receive combiner (RC) are determined at the dual-function radar-communication (DFRC) base station (BS) and also at the user equipment (UE) for maximizing the performance attained. The Bayesian Cramer-Rao bounds (BCRB) are explicitly derived to benchmark the performance of the wireless channel and radar target parameter estimation. Simulation results are provided to demonstrate the improved performance of the proposed schemes considering multiple metrics, such as the normalized mean squared error (NMSE), bit error rate (BER) and achievable spectral efficiency (ASE).

Asynchronous distributed hybrid beamformers (ADBF) are conceived for minimizing the total transmit power subject to signal-to-interference-plus-noise ratio (SINR) constraints at the users. Our design requires only limited information exchange between the base stations (BSs) of the mmWave multi-cell coordinated (MCC) networks considered. To begin with, a semidefinite relaxation (SDR)-based fully-digital (FD) beamformer is designed for a centralized MCC system. Subsequently, a Bayesian learning (BL) technique is harnessed for decomposing the FD beamformer into its analog and baseband components and construct a hybrid transmit precoder (TPC). However, the centralized TPC design requires global channel state information (CSI), hence it results in a high signaling overhead. An alternating direction based method of multipliers (ADMM) technique is developed for a synchronous distributed beamformer (SDBF) design, which relies only on limited information exchange among the BSs, thus reducing the signaling overheads required by the centralized TPC design procedure. However, the SDBF design is challenging, since it requires the updates from the BSs to be strictly synchronized. As a remedy, an ADBF framework is developed that mitigates the inter-cell interference (ICI) and also control the asynchrony in the system. Furthermore, the above ADBF framework is also extended to the robust ADBF (R-ADBF) algorithm that incorporates the CSI uncertainty into the design procedure for minimizing the the worst-case transmit power. Our simulation results illustrate both the enhanced performance and the improved convergence properties of the ADMM-based ADBF and R-ADBF schemes.

Hybrid precoders and combiners are designed for cooperative cell-free multi-user millimeter wave (mmWave) multiple-input multiple-output (MIMO) cellular networks for low complexity interference mitigation. Initially, we derive an optimal hybrid transmit beamformer (HTBF) for a broadcast scenario considering both total and per access point (AP) power constraints. Next, an optimal successive hybrid beamformer technique is proposed for unicast and multicast scenarios which relies on the optimal minimum variance distortionless response (MVDR). We demonstrate that it mitigates both the interuser and intergroup interference, while successively ensuring orthogonality to the previously scheduled users/user groups. Furthermore, it is shown theoretically that the proposed schemes are capable of supporting a large number of users. Subsequently, a Bayesian learning (BL) based method is conceived for jointly designing the RF and baseband precoders/combiners for the various scenarios considered. Furthermore, we also conceive the uplink counterpart of our HTBF scheme, which is based on maximizing the signal-to-interference-plus noise ratio (SINR) of each individual user. Finally, the efficacy of the proposed schemes is characterized by our extensive simulation results in terms of cancelling the interuser/intergroup interference, which improves the spectral efficiency.

This work conceives novel target detection and parameter estimation schemes in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) radar (mMR) systems for both stationary and mobile targets/radar platform. Initially, the orthogonal matching pursuit (OMP)-based mmR (OmMR) algorithm is proposed for stationary targets to estimate their radar cross-section (RCS) coefficients, angle, range locations together with the number of targets. Next, mMR systems with mobile targets and platform are considered, followed by development of the simultaneous OMP (SOMP)-based mMR (SmMR) algorithm for RCS, angle/range estimation together with their Doppler velocities. The proposed algorithms lead to a significant improvement in performance since they exploit the inherent sparsity of the mMR scattering scene in contrast to the conventional schemes. Two-dimensional (2D) mMR imaging procedures are also presented for both scenarios in the angle, range, and Doppler dimensions. Analytical expressions are derived for the Cramér-Rao bounds (CRBs) for the mean-squared error (MSE) of joint estimation of the RCS coefficients and Doppler velocities. Simulation results demonstrate that proposed schemes perform well even in low signal-to-noise ratio (SNR) scenarios with a few snapshots of the scattering environment and yield improved performance in comparison to existing sparse as well as non-sparse schemes.