A user equipment (UE)-assisted hybrid coherent and noncoherent transmission scheme is designed for cell-free massive multiple-input–multiple-output (MIMO), operating in the presence of phase offsets caused by both imperfect radio frequency (RF) chains and asynchronous reception. First, considering the effects of both factors, we derive closed-form spectral efficiency (SE) expressions for hybrid transmission under conjugate beamforming and zero-forcing (ZF) precoders. Based on these expressions, we introduce the concept of superposition gain to clarify the rationality of hybrid transmission. To leverage its advantages, we propose an access point (AP) grouping algorithm and its enhanced version, which groups the serving APs based on downlink (DL) equivalent channel at the UE side. Since the DL equivalent channel incorporates information on both RF and delay phase offsets, hybrid transmission based on this algorithm can simultaneously address both. Additionally, to implement this UE-assisted hybrid transmission with low overhead, we propose a signaling interaction strategy that extends traditional processes by introducing ZF-based DL beamforming pilot transmission for obtaining the DL equivalent channel, along with indication method for reporting the grouping results. Moreover, to further improve performance, a sequential convex approximation power allocation algorithm is proposed for hybrid transmission. Finally, simulations show that UE-assisted hybrid transmission achieves nearly fivefold improvement in 95%-likely SE over coherent transmission in the presence of both RF and delay phase offsets.

The upper mid-band, spanning from 7 GHz to 20 GHz, emerges as a promising candidate spectrum for 6G, offering extensive coverage and remarkable capacity. It drives massive Multiple Input Multiple Output (MIMO) to adopt extremely large antenna arrays and the so-called extreme massive MIMO will play an essential role. However, as the number of antennas at base stations increases, the demands on signal processing intensify significantly, imposing substantial limitations on product implementation. Reduced-rank beamforming such as beamspace beamforming can reduce the complexities associated with channel estimation and precoding. This paper introduces a low-complexity block Krylov-beamspace beamforming, targeting the implementation of two-stage long-term/short-term fully digital rank-reduction beamforming in 6G extreme massive MIMO at upper mid-band. The key lies in the concept of using the derived multiple signature vectors to calculate the beamspace beamforming matrix that spans the block Krylov subspace. An adaptive and fast-converging beamspace determination scheme is also developed for the proposed algorithm. We further investigate its implementation in the practical cross-polarized antenna systems. The proposed beamforming is able to provide an eigen-based performance but with a significant complexity reduction, exhibiting a great potential in the evolution of extreme massive MIMO for 6G.

Cell-Free massive multiple-input multiple-output (MIMO), which provides high spectral efficiency (SE) through the coherent joint transmission, has drawn much academic research interest. However, the implementation of coherent joint transmission is restricted by the phase offset caused by hardware defect and asynchronous reception. In this paper, by combining the advantages of the high performance provided by coherent joint transmission and the phase offset robustness provided by non-coherent joint transmission, we propose a hybrid transmission scheme based on access point (AP) grouping, which ensures decent coherent joint transmission by minimizing the phase offset between collaborative APs within the group, and avoids large phase offset between groups by executing non-coherent joint transmission. Moreover, to facilitate simulation verification of the performance of the proposed scheme under various preprocessing methods, we derive a generalized version of SE expression of the hybrid transmission scheme using successive interference cancellation. Furthermore, simulations are provided to verify the effectiveness of our proposed scheme.

Deep learning (DL) based receiver (DeepRx) has been proven to be able to greatly improve the data transmission performance compared to conventional OFDM (orthogonal frequency-division multiplexing) receivers. In order to accommodate DFT-s-OFDM (discrete Fourier transform-spread OFDM), which is a widely used single carrier waveform for uplink transmission characterized with low peak to average power ratio (PAPR), the internal structure of DeepRx needs to be redesigned. In this paper, we propose a novel deep neural network based receiver, referred to as IDFT-deprecoding embedded deep receiver (iDeepRx) customized for DFT-s-OFDM. By embedding an untrainable functionality of IDFT-deprecoding between two trainable neural network structures, the proposed iDeepRx can support DFT-s-OFDM transmission and mitigate implicit link and hardware-induced channel impairments. We demonstrate a single-layer 100 Gb/s error-free data transmission using iDeepRx over 20 GHz bandwidth at 220 GHz carrier frequency and observe 2 dB+ relative performance gain over the conventional receiver. Moreover, intermediate output of iDeepRx has been extracted with constellation-like patterns for visualization and performance monitoring during training and inference, which helps in making the internal working mechanism of iDeepRx more explainable.

Massive Multiple Input Multiple Output (MIMO) is able to boost the system throughput. A key challenge is a large overhead of Channel State Information (CSI) feedback with the increased number of antenna ports in Frequency Division Duplexing (FDD) massive MIMO systems. Conventional methods apply either compressed sensing or beamformed reference signal to reduce the CSI overhead. However, there are still other problems such as additional overhead, user's implementation complexity, or performance limitation. We propose a machine learning enhanced CSI acquisition and training solution for FDD massive MIMO. It can efficiently recover the CSI with more ports than those of the CSI feedback. Furthermore, a practical training strategy is developed, which shows the feasibility of using uplink dataset to train the neural network for the downlink use in FDD.

Massive Multiple Input Multiple Output (MIMO) at millimeter wave bands is able to boost the system throughput. A key challenge for the hybrid beamforming design in massive MIMO systems is the acquisition of the full channel state information, since the number of radio frequency chains is much smaller than that of the antennas. Conventional methods require a longer measurement time, a large overhead, or costly signal processing efforts. Therefore, we propose an efficient and adaptable deep neural network based low-rank channel recovery scheme for a hybrid array based massive MIMO system. The proposed neural network architecture includes a common feature extraction module and the adaptable recovery module. The feature extraction, built on the convolutional neural network with residual learning functionality, can efficiently learn the essential features from the low-rank measurements. The adaptable key recovery module maps the essential features to the full channel information. The proposed architecture enables an efficient learning procedure and can be easily adapted to different cases. Simulation results are carried out and compared with existing solutions, showing the potential of applying deep learning concepts in millimeter wave massive MIMO systems.

Applying relays in the multi-user massive Multiple Input Multiple Output (MIMO) system at Millimeter Wave (mmWave) bands can greatly help to overcome the large propagation loss at higher frequencies and boost the communication link. We propose an efficient hybrid beamforming technique for the relay assisted mmWave multi-user massive MIMO system. The design includes the analog transmit-receive coordinated beam alignment procedure and non-linear precoding based digital beamforming. The analog beam alignment can be flexibly applied to different hybrid array architectures. We propose to jointly design digital beamforming cross the base station, the relay, and the multi-antenna users to enhance the system performance. It is based on the Geometric Mean Decomposition Tomlinson Harashima Precoding (GMD-THP) scheme, and only linear processing is applied at the relay for a cost-effective reason. We evaluate the performance of the proposed technique and compare it with its linear and fully digital counterparts.

To assist hybrid beamforming in multi-user massive Multiple Input Multiple Output (MIMO) systems at millimeter wave frequencies, we propose Auxiliary Processing Network (APN) based multi-phase channel alignment techniques. The APN, which is realized in parallel to the main beamforming network, have two implementation architectures, namely the hybrid analog-digital APN based on antenna switching as well as the fully digital APN with low-resolution Analog-to-Digital Converters (ADCs). For different APN architectures, we design various channel estimation algorithms for the channel alignment procedure and evaluate the corresponding performance.

Multi-beam mobile satellite systems aim at providing broadband and high speed mobile services over a large area to achieve a high system throughput, where hybrid space-ground beamforming is one of the most promising candidates for ground-based beamforming techniques. It not only reduces the feeder link bandwidth to save spectral resources, but also takes advantages of both the on-ground and on-board processing, exhibiting a good trade-off of the performance and the space/ground complexity. In this paper, we propose an efficient hybrid space-ground precoding technique for multi-beam mobile satellite systems. It consists of coarse on-board beamforming and reduced-rank on-ground beamforming based on the feed selection of the phased array antenna. The advantages of the proposed hybrid precoding are shown as compared to the fully on-ground beamforming as well as the existing solutions.

This paper considers a low-complexity hybrid beamforming (HBF) for uplink multiuser mmWave MIMO systems. The proposed scheme employs a two-stage beamforming design. In the first stage, the base station (BS) analog beamforming is designed to maximize the $k$th user's signal-to-interference-plus-noise-ratio (SINR) in single-carrier systems and to maximize the sum rate's upper bound of uplink virtual cooperative users in orthogonal frequency division multiplexing (OFDM) based systems (also called multi-carrier systems), respectively. Furthermore, we perform phases extraction and quantize phase control on the obtained BS analog beamforming. In the second stage, the BS digital beamforming is designed by performing minimum mean square error (MMSE) with the low-dimensional effective channel. The proposed schemes are simulated in both ideal Rayleigh fading channels and sparsely scattered mmWave channels, approaching the sum rate performance of the full digital solution in single-carrier case and of the virtual uplink user cooperation scheme in multi-carrier case, respectively. The simulation results demonstrate that the proposed schemes outperform the existing HBF designs for uplink multiuser mmWave MIMO systems.