Towards 6G: Spectrally efficient joint radar and communication with radio frequency selection, interference and hardware impairments (invited paper)

Abstract

The joint radar‐communication (JRC) system is envisioned as an emerging sixth generation (6G) technology to tackle spectral congestion and hardware limitations by jointly implementing the communication and radar sensing on the same hardware platform and using the common radio frequency (RF) resources. Joint radar‐communication systems with a multi‐antenna setup leads to higher degrees of freedom, and hybrid beamforming can be exploited to achieve lower hardware complexity than conventional fully digital systems. This paper aims to design a spectral efficiency maximisation approach for a 6G inclined JRC system with hybrid beamforming and multi‐antenna setup while considering the interference between communication and radar operations and hardware impairments in the system. The rate expressions for communication and radar operations are defined, and the joint spectral efficiency is maximised via optimising the number of RF chains using an efficient selection algorithm taking into account the interference of one operation to the other and system hardware distortion. The simulation results are shown to support the effectiveness of the proposed approach, and they are compared with that of existing fully digital and hybrid beamforming based baseline methods with fixed number of RF chains. The proposed approach also exhibits a desirable communication‐radar trade‐off in terms of spectral efficiency gains.

Optimized wireless communication for 6G signal processing using memory‐augmented deep unfolding network

“…When a measure yields consistent, comparable outcomes under regular circumstances, it is deliberated to have higher reliability. And it is given as Equation (14).…”

“…13 It facilitates the extraction of important patterns and insights from massive datasets, improving our comprehension of the propagation and interaction of signals within these networks. 14 Building on these achievements, deep learning applications keep growing into increasingly specialized domains. For instance, it has been applied to the study of criminal networks, assisting in the comprehension of their composition and methods of functioning.…”

“…Signal propagation in intricate networks is one of the many domains to which deep learning data analysis may be used 13 . It facilitates the extraction of important patterns and insights from massive datasets, improving our comprehension of the propagation and interaction of signals within these networks 14 . Building on these achievements, deep learning applications keep growing into increasingly specialized domains.…”

Millimeter Wave Channel Estimation for Lens based Hybrid MIMO with Low Resolution ADCs

“…Such systems can also be optimized to achieve high energy efficiency (EE) [5]- [7]. More recent applications of low resolution DACs, RF optimization and low complexity architectures has been into the next generation joint radarcommunication systems [8]- [12].…”

Flexible Hybrid Beamforming for Spectrally Efficient 6G Joint Radar-Communications

“…One such approach is to unify sensing and communications operations on a single hardware and use of same spectral resources, where sensing collects and extracts information, such as target detection, and using radio waves for tracking movement, while communication possesses transfer of information [3]- [5]. The emerging joint radar and communications (JRC) systems accommodate multiple functionalities of these operations, massive connectivity with limited resources, and provide wide applications in future wireless, defence, aerial, vehicular, internet-of-things and space [4]- [7]. To achieve high beamforming gains with high degrees of freedom, JRC can employ multiple-input multiple-output (MIMO) antennas which improves range resolution and also makes up for the high path loss associated with high-frequency bands such as millimeter wave [8], [9].…”