This paper proposes a joint uplink massive multiple-input-multiple-output (MIMO) communication and orthogonal frequency-division multiplexing (OFDM) radar sensing architecture. Specifically, uplink communication and short-range radar sensing are considered, where the user equipments (UEs) transmit data to the base-station (BS), which simultaneously receives radar returns from the targets over the same subcarriers. Hence, the signals received at each BS antenna include radar returns and communication signals to be processed for extracting the sensing and communication data. The separation and detection of such signals are achieved by utilizing the channel diversity between the UEs and the targets. To this end, the UEs’ signals are first detected and demodulated, and then subtracted from the received signal to acquire the radar returns. Symbol-based radar processing is then employed, as it provides substantial processing gains, and its detection performance is independent of the transmitted radar waveform. Furthermore, self-interference—due to the simultaneous operation of transmit and receive antennas—is taken into account. The communication rate and normalized error of the radar-target channel estimation are mathematically analyzed, and the trade-off between the achievable rate and radar detection performance is demonstrated in terms of the output power of the communication and radar sub-systems and the signal-to-noise ratio.

In this study, a dual-functional radar and communication (RadCom) system architecture is proposed for application at base-stations (BSs), or access points (APs), for simultaneously communicating with multiple user equipments (UEs) and sensing the environment. Specifically, massive multiple-input multiple-output (mMIMO) communication and orthogonal frequency-division multiplexing (OFDM)-based MIMO radar are considered with the objective to jointly utilize channel diversity and interference. The BS consists of a mMIMO antenna array, and radar transmit and receive antennas. Employing OFDM waveforms for the radar allows the BS to perform channel state information (CSI) estimation for the mMIMO and radar antennas simultaneously. The acquired CSI is then exploited to predict the radar signals received by the UEs. While the radar transmits an OFDM waveform for detecting possible targets in range, the communication system beamforms to the UEs taking into account the predicted radar interference. To further enhance the capacity of the communication system, an optimum radar waveform is designed. Moreover, the network capacity is mathematically analyzed and verified by simulations. The results show that the proposed RadCom can achieve higher capacity than conventional mMIMO systems by utilizing the radar interference while simultaneously detecting targets.

This paper considers the optimization of a dual-functional radar and communication (RadCom) system with the objective is to maximize its sum-rate (SR) and energy-efficiency (EE) while satisfying certain radar target detection and data rate per user requirements. To this end, novel RadCom precoder schemes that can exploit downlink radar interference are devised for massive multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. First, the communication capacity and radar detection performance metrics of these schemes are analytically evaluated. Then, using the derived results, optimum beam power allocation schemes are deduced to maximize SR and EE with modest computational complexity. The validity of the analytical results is confirmed via matching computer simulations. It is also shown that, compared to benchmark techniques, the devised precoders can achieve substantial improvements in terms of both SR and EE.

This study proposes a dual-function radar and communication (DFRC) system that utilizes radar transmission parameters as modulation indexes to transmit data to the users while performing radar sensing as its primary function. The proposed technique exploits index modulation (IM) using the center frequency of radar chirps, their bandwidths, and polarization states as indexes to modulate the communication data within each radar chirp. By utilizing the combination of these indexes, the proposed DFRC system can reach up to 17 Mb/s throughput, while observing a robust radar performance. Through our experimental study, we also reveal the trade-off between the radar sensing performance and communication data rate, depending on the radar waveform parameters selected in the DFRC system. This study also demonstrates the implementation of the proposed DFRC system and presents its real-time over-the-air experimental measurements. Consequently, the simulation results are verified by real-time over-the-air experiments, where ARESTOR, a high-speed signal processing and experimental radar platform, has been employed.

This article comprehensively reviews recent developments and research on deep learning-based (DL-based) techniques for integrated sensing and communication (ISAC) systems. ISAC, which combines sensing and communication functionalities, is regarded as a key enabler for 6G and beyond networks, as many emerging applications, such as vehicular networks and industrial robotics, necessitate both sensing and communication capabilities for effective operation. A unified platform that provides both functions can reduce hardware complexity, alleviate frequency spectrum congestion, and improve energy efficiency. However, integrating these functionalities on the same hardware requires highly optimized signal processing and system design, introducing significant computational complexity when relying on conventional iterative or optimization-based techniques. As an alternative to conventional techniques, DL-based techniques offer efficient and near-optimal solutions with reduced computational complexity. Hence, such techniques are well-suited for operating under limited computational resources and low latency requirements in real-time systems. DL-based techniques can swiftly and effectively yield near-optimal solutions for a wide range of sophisticated ISAC-related tasks, including waveform design, channel estimation, sensing signal processing, data demodulation, and interference mitigation. Therefore, motivated by these advantages, recent studies have proposed various DL-based approaches for ISAC system design. After briefly introducing DL architectures and ISAC fundamentals, this survey presents a comprehensive and categorized review of state-of-the-art DL-based techniques for ISAC, highlights their key advantages and major challenges, and outlines potential directions for future research.

Multi-user massive multiple-input-multiple-output (massive MIMO) will play a key role in future wireless communication networks. Since spatial channel diversity is the fundamental merit of this technique, high channel correlation may significantly restrict its abilities. This study investigates the impact of channel correlation on a prototyped massive MIMO network with the objective to identify an antenna array geometry which has reduced mutual coupling and channel correlation. To this end, a highly-efficient directional wideband single antenna element was designed for the antenna arrays and the user equipments (UEs). The designed array geometry is tested in an experimental indoor wideband massive MIMO setup. Important system parameters, such as channel correlation, power delay profile, and average received power from the UEs, are studied by analyzing the measured channel data. Furthermore, system-level simulations and network capacity calculations are performed based on the measured channel data to evaluate the performance of the prototyped antenna arrays. A regular array was also fabricated and used for benchmarking comparison. Moreover, a power control algorithm is introduced for the uplink, which was shown to improve the network capacity by up to 3 dB. The results demonstrate that the introduced antenna array outperforms the uniform antenna array in terms of mutual coupling and channel capacity.