For data rates exceeding several gigabits per second, which are achievable in fifth-generation wireless networks, the integration of subterahertz (sub-THz) communications has become increasingly important. However, hardware limitations, such as phase noise arising from local oscillator imperfections, can hinder the performance of sub-THz systems. This paper addresses the challenge of phase noise compensation for data decoding in orthogonal frequency division multiplexing systems. To address this issue, we propose a new sequential feature learning mechanism designed to capture both the subcarrier-shift invariance feature of phase noise and the subcarrier-dependent feature of intercarrier interference. The proposed approach involves empirical performance evaluations under various conditions and a theoretical analysis that considers computational complexity and the signal-to-interference-plus-noise power ratio. By performing comprehensive comparisons with existing analytical and deep learning-based methods, we demonstrate the effectiveness of our proposed method, even with an extremely low pilot overhead.

Since the introduction of fifth-generation new radio (5G-NR) in Third Generation Partnership Project (3GPP) Release 15, swift progress has been made to evolve 5G with 3GPP Release 18 emerging. A critical aspect is the design of massive multiple-input multiple-output (MIMO) technology. In this line, this paper makes several important contributions: We provide a comprehensive overview of the evolution of standardized massive MIMO features from 3GPP Release 15 to 17 for both time/frequency-division duplex operation across frequency range in (FR)-1 and FR 2-1. We analyze the progress on channel state information (CSI) frameworks, beam management frameworks and present enhancements for uplink CSI. We shed light on emerging 3GPP Release 18 problems requiring imminent attention. These include advanced codebook design and sounding reference signal design for coherent joint transmission (CJT) with multiple transmission/reception points (multi TRPs). We discuss advancements in uplink demodulation reference signal design, enhancements for mobility to provide accurate CSI estimates, and unified transmission configuration indicator framework tailored for FR 2–1 bands. For each concept, we provide system level simulation results to highlight their performance benefits. Via field trials in an outdoor environment at Shanghai Jiaotong University, we demonstrate the gains of multi-TRP CJT relative to single TRP at 3.7 GHz.

In this paper, we consider a distributed multi-input-multi-output (D-MIMO) system wherein multiple radio remote heads (RRHs) distributed in a cell are connected with a single baseband unit. To enable coherent joint transmission from multiple RRHs in the D-MIMO system, we propose several channel state information (CSI) codebooks as candidates for enhancements in the context of 3rd Generation Partnership Project (3GPP) 5G New Radio (NR) standardization. The proposed codebooks are developed based on the 5G Release-16 Type-II CSI codebook framework. In addition, we propose dynamic RRH selection (DRS) methods that are able to obtain performance gain and reduce the amount of feedback by sending the CSI only for the selected RRHs having dominant channel qualities. System-level simulation (SLS) results under realistic scenarios are provided to validate the potential of the proposed CSI codebooks and DRS methods.

In this paper, we propose a two-stage analog combining architecture for millimeter wave (mmWave) communications with hybrid analog to digital beamforming and low-resolution analog-to-digital converters (ADCs). We first derive a two-stage combining solution by solving a mutual information (MI) maximization problem without a constant modulus constraint on analog combiners. With the derived solution, the proposed receiver architecture splits the analog combining into a channel gain aggregation stage followed by a spreading stage to maximize the MI by effectively managing quantization error. We show that the derived two-stage combiner achieves the optimal scaling law with respect to the number of radio frequency (RF) chains and maximizes the MI for homogeneous singular values of a MIMO channel. Then, we develop a two-stage analog combining algorithm to implement the derived solution under a constant modulus constraint for mmWave channels. Simulation results validate the algorithm performance in terms of MI.

We investigate uplink user scheduling for millimeter wave (mm-wave) hybrid analog/digital beamforming systems with low-resolution analog-to-digital converters (ADCs). Deriving new scheduling criteria for the mm-wave systems, we show that the channel structure in the beamspace, in addition to the channel magnitude and orthogonality, plays a key role in maximizing the achievable rates of scheduled users due to quantization error. The criteria show that to maximize the achievable rate for a given channel gain, the channels of the scheduled users need to have 1) as many propagation paths as possible with unique angle-of-arrivals (AoAs) and 2) even power distribution in the beamspace. Leveraging the derived criteria, we propose an efficient scheduling algorithm for mm-wave zero-forcing receivers with low-resolution ADCs. We further propose a chordal distance-based scheduling algorithm that exploits only the AoA knowledge and analyze the performance by deriving ergodic rates in closed form. Based on the derived rates, we show that the beamspace channel leakage resulting from phase offsets between AoAs and quantized angles of analog combiners can lead to sum rate gain by reducing quantization error compared to the channel without leakage. The simulation results validate the sum rate performance of the proposed algorithms and the derived ergodic rate expressions.

In this paper, a new two-phase-feedback-based user-scheduling-and-beamforming method is proposed for multi-user multiple-input multiple-output downlink in the context of two-stage beamforming. The key ideas of the proposed method are: 1) to use a set of orthogonal reference beams and construct a cone around each reference beam to select “nearly-optimal” semi-orthogonal users based only on channel quality indicator feedback; and 2) to apply post-user-selection beam design with zero-forcing beamforming (ZFBF) based on channel state information (CSI) feedback only from the selected users. It is proven that the proposed scheduling-and-beamforming method is asymptotically optimal as the number of users increase. Furthermore, the proposed scheduling-and-beamforming method almost achieves the performance of the existing semi-orthogonal user selection with ZFBF that requires full CSI for all users, with a significantly reduced amount of required channel information which is even less than that required by random beamforming.

Wireless communication is now moving to automotive systems. In the near future, vehicles and road side units (RSUs) will be equipped with various sensors such as radars, LIDARs, cameras, and GPSs to generate enormous sensor information. To enhance the safety and efficiency, they will share this raw sensor information each other through Gbps data rates using millimeter-wave (mmWave) communications. To realize this vision, two unfavorable attributes of mmWave communications should be addressed: 1) Overcoming blockage and 2) fast link configuration. One viable approach to addressing these issues is to exploit RSU equipped with many sensors. At a higher position such as at the top of the RSU, LoS links between vehicles and the RSU are more likely to be guaranteed as well as it facilitates the extraction of useful information for the fast link configuration such as the distances or the angles between vehicles and the RSU using the sensors. In this paper, we assume that from the sensors (aside from the relevant technology) the angular support information for the LoS link of each vehicle is known to the RSU in prior and we propose a fast link configuration method using the angular support information.

In this paper, the performance of opportunistic random beamforming (RBF) and the multiuser (MU) gain in millimeter-wave (mm-wave) MU multiple-input single-output (MISO) downlink systems are analyzed based on the uniform random single-path (UR-SP) channel model suitable for highly directional mm-wave radio propagation channels. It is shown that under the UR-SP channel model, RBF achieves linear sum rate scaling with respect to (w.r.t.) the number of transmit antennas and, furthermore, yields optimal sum rate performance when the number of transmit antennas is large, if the number of users increases linearly w.r.t. the number of transmit antennas. Several beam training and user selection methods are investigated to yield insights into the most effective beamforming and scheduling choice for mm-wave MU-MISO in various operating conditions. Simulation results validate our analysis based on asymptotic techniques for finite cases.

In this paper, the problem of outer beamformer design based only on channel statistic information is considered for two-stage beamforming for multi-user massive MIMO downlink, and the problem is approached based on signal-to-leakage-plus-noise ratio (SLNR). To eliminate the dependence on the instantaneous channel state information, a lower bound on the average SLNR is derived by assuming zero-forcing (ZF) inner beamforming, and an outer beamformer design method that maximizes the lower bound on the average SLNR is proposed. It is shown that the proposed SLNR-based outer beamformer design problem reduces to a trace quotient problem (TQP), which is often encountered in the field of machine learning. An iterative algorithm is presented to obtain an optimal solution to the proposed TQP. The proposed method has the capability of optimally controlling the weighting factor between the signal power to the desired user and the interference leakage power to undesired users according to different channel statistics. Numerical results show that the proposed outer beamformer design method yields significant performance gain over existing methods.