Integrated Sensing and Communication With mmWave Massive MIMO: A Compressed Sampling Perspective

Abstract

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the highdimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.

Secure wireless communications for STAR‐RIS‐assisted millimetre‐wave NOMA uplink networks

“…MmWave communication has been considered as a pivotal technique [18] due to the vast available spectrum. Employing a NOMA scheme in the mmWave communication system can support more than one user to transmit signals simultaneously in the same time-frequency-code-space resource block, enabling large-scale user connections and improving spectrum efficiency [14][15][16].…”

“…Up to this point, since the rank-one constraint (9g) is the only non-convex part in the optimization problem (18), we apply the SDR technique to drop the rank constraint in (9g). It can be proved that the solutions {W} obtained for the relaxed version of problem ( 18) always satisfy Rank(W) = 1 without loss of optimality [37].…”

SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems

“…mmWave bistatic JCS. A few works have addressed bistatic JCS in mmWave systems, assuming synchronized transmitter and receiver [27], [28], [29]. Others have focused on tracking targets based on distance and Angle of Arrival (AoA), without addressing sensing tasks that require phase analysis or aggregation of signal samples over coherent processing intervals, such as Doppler speed estimation [30].…”

Engineering Mathematics Course in Department of Electrical Engineering at Southern University

“…2 Even if the system works in half duplex mode [27], for the normal communications distance, it can still be considered that the transmitting antenna array and the receiving antenna array are at the same location. Hence there is still wr = wt when the two arrays have the same configuration and are placed in parallel.…”

“…One can easily extend this work to the half duplex mode, in which the BS has separate transmitting and receiving antenna array with certain protection distance to eliminate the self-interference[27]. Nevertheless, we choose full duplex assumption here mainly to illustrate the proposed YOLO algorithm in a clearer way.…”