ABSTRACT Channel state information (CSI) is pivotal for assuring high performances of wireless communication systems. In particular, multiple‐input multiple‐output transmission is only beneficial when CSI is known. A large number of subcarriers are desired in Orthogonal Frequency Division Multiplex (OFDM) systems to boost overall throughput, which makes channel estimation a more challenging task, especially to extract channel features in a more dynamic environment without causing a significant overhead transmission. Conventional least squares‐based methods are affected by the noise and interference that inherently exist in the acquired data for processing. We proposed the deep neural network (DNN)‐based method to estimate CSI, and one distinguishing characteristic is to adopt a Discrete Fourier Transform (DFT) operation‐based method to mitigate the impact of noise before carrying out the DNN procedure; hence, the accuracy of the learning outcome significantly improved. The effectiveness of the proposed scheme is verified with simulations under a variety of propagation scenarios. The proposed method has demonstrated a high performance for channel estimation. It has shown a particular advantage in more dynamic and noisy environments for wireless communications.

This study proposes a radar-centric integrated sensing and communication (ISAC) system that utilizes a two-layer modulation scheme for vehicular networks. Frequencymodulated continuous wave (FMCW) chirps are jointly modulated via phase modulation (PM) and index modulation (IM) to transmit data while maintaining sensing as the primary function. Moreover, a novel radar signal processing technique is developed to mitigate the impacts of IM and PM on sensing accuracy, alongside a communication receiver architecture designed to demodulate IM and PM data within FMCW chirps successfully. System performance is evaluated through simulations in the 2.4 GHz and 24 GHz bands under Doppler effects, achieving communication throughputs of 25 Mbps and 50 Mbps, respectively. Furthermore, a proof-of-concept hardware implementation is realized, and experimental measurements are performed via a loopback cable to verify the feasibility of the architecture. Finally, it evaluates the fundamental trade-off between communication throughput, sensing accuracy, and out-of-band emission, demonstrating the system’s flexibility to dynamically adjust waveform parameters to meet various operational requirements.

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 examines the uplink performance of large‐constellation multi‐user massive multiple‐input multiple‐output systems with low‐resolution analogue‐to‐digital converters (ADCs) in the presence of channel correlation and imperfect channel state information (CSI). The base station (BS) employs a large number of antennas for multiplexing and demultiplexing co‐channel users with each antenna element having a dedicated radio frequency chain and two low‐resolution ADCs. While such ADCs cause data loss due to coarse quantisation, the large number of antennas can be exploited not only to alleviate such a problem but also to make it possible to utilise large‐constellation modulation schemes. The results provide an insight into the trade‐off between various performance metrics and the number of quantisation bits under a wide range of realistic conditions. It will be shown that 1‐bit quantisation provides sufficient resolution with 100 BS antennas to communicate with ten user equipments using quadrature phase shift keying, but the number of quantisation bits must be increased for larger constellations particularly to overcome CSI mismatch and channel correlation. The results also consider the trade‐off between average mutual information and power consumption of the low‐resolution ADCs. It will be shown that 16‐quantum amplitude modulation with 2‐bit quantisation may provide a good compromise between energy efficiency and average mutual information.