In this work, we propose a novel energy-efficient spiking neural network (SNN)-based receiver for 5G-NR OFDM system, called neuromorphic receiver (NeuromorphicRx), replacing the channel estimation, equalization and symbol demapping blocks. We leverage domain knowledge to design the input with spiking encoding and propose a deep convolutional SNN with spike-element-wise residual connections. We integrate an SNN with artificial neural network (ANN) hybrid architecture to obtain soft outputs and employ surrogate gradient descent for training. We focus on generalization across diverse scenarios and robustness through quantized aware training. We focus on interpretability of NeuromorphicRx for 5G-NR signals and perform detailed ablation study for 5G-NR signals. Our extensive numerical simulations show that NeuromorphicRx is capable of achieving significant block error rate performance gain compared to 5G-NR receivers and similar performance compared to its ANN-based counterparts with 7.6× less energy consumption.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) is one of the most promising technologies from 5G-based Internet of Things (IoT) to future wireless communication-based IoT, which usually relies on beamforming codebooks for data transmission. However, traditional codebooks often consist of numerous narrow beams, which causes substantial training overhead. Although centralized machine learning-based methods can address this issue to some extent, they overlook minority IoT devices scattered across various areas, which is vital for the coverage equity of the environmental adaptive codebook and the optimal average achievable rate. To circumvent the problem, we propose a distributed learning (DL) framework for codebook design in mmWave massive MIMO systems with uneven user distribution. Specifically, the user channel set is first divided into subsets by pre-classification based on the power responses of the featured combining vectors from different subregions. Then, a novel DL architecture processes these subsets, each assigned to different baseband processing boards (BPBs) in building baseband units, alleviating the centralized machine learning burden on the active antenna unit (AAU) or its directly connected BPB. Meanwhile, the current algorithms lack the hardware perspective or only implement the inference stage of the model. Thus, we deploy an FPGA-adapted DL-based codebook training prototype that runs on FPGA, which fully explores the “Backward-While-Forward" strategy for data reuse in the forward and backward passes. Simulation validates the effectiveness of distributed learning. Notably, the FPGA implementation on the embedded-level board outperforms consumer-grade CPU and GPU in terms of both latency and energy efficiency.

A new sidelobe suppression method is proposed for filter bank multi-carrier systems using offset quadrature amplitude modulation (FBMC-OQAM), where we jointly optimize the prototype filter and the symbol distribution to minimize the normalized stopband energy of FBMC-OQAM signals. Firstly, we investigate the relationship between both the signal sidelobe and the prototype filter as well as the symbol distribution by deriving the general power spectral density (PSD) expression of the FBMC-OQAM signals of each subcarrier. Then, we investigate the impact of the prototype filter and the symbol distribution on the symbol reconstruction by deriving the inter-symbol interference (ISI) and inter-carrier interference (ICI) expressions of FBMC-OQAM systems. Based on the PSD and ISI/ICI expressions derived, we formulate and solve the joint prototype filter and symbol distribution optimization problem. Our simulations demonstrated that the proposed joint prototype filter and symbol distribution optimization method achieves lower normalized stopband energy and better sidelobe suppression of the first sidelobe than the single-parameter based prototype filter optimization methods.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) technology represents a promising technology in wireless communication. This technology relies on beamforming codebooks for initial access and transmission. However, conventional codebooks comprise a multitude of single-lobe narrow beams, resulting in redundant beams that may never be utilized in beam training. While centralized machine learning methods can partially address the concern of redundancy, they tend to overlook the presence of minority users scattered across diverse regions. The equitable coverage of environmental adaptive codebooks depends on addressing this issue. Hence, we devise a distributed learning (DL) framework for codebook design, which is tailored for scenarios with uneven user distribution and fully exploits the decentralized and online learning features of DL. Our approach begins by segmenting the user channels into various subsets through a pre-classification process. Then, we introduce a novel DL architecture designed to process the subsets that are assigned to individual user equipments (UEs). Each UE then generates a phase shift matrix that contributes to the concatenation-based global aggregation in the base station. The simulation results confirm the effectiveness of DL in improving the performance of mmWave massive MIMO systems in scenarios with unevenly distributed users.

In this paper, we focus our attention on the mitigation of beam squint for reconfigurable intelligent surface (RIS) aided wideband millimeter wave (mmWave) communications. Due to the intrinsic passive property, the phase shifts of all elements in RIS should be the same for all frequencies. However, in the wideband scenario, beam squint induced distinct path phases require designing different phase shifts for different frequencies. The above irreconcilable contradiction will dramatically affect the system performance, considering the RIS usually consists of enormous elements and the bandwidth of wideband mmWave communications may be up to several GHz. Therefore, we propose some novel phase shift design schemes for mitigating the effect of beam squint for both line-of-sight (LoS) and non-Los (NLoS) scenarios. Specifically, for the LoS scenario, we firstly derive the optimal phase shift for each frequency and obtain the common phase shift by maximizing the upper bound of achievable rate. Then, for the NLoS scenario, a mean channel covariance matrix (MCCM) based scheme is proposed by fully exploiting the correlations between both the paths and the subcarriers. Our extensive numerical experiments confirm the effectiveness of the proposed phase shift design schemes.

Hybrid Transmit Precoding (TPC) is one of the most compelling solutions for millimeter wave (mmWave) multiple-input multiple output (MIMO) systems. However, most attention has been focused on narrow-band scenarios. Hence, we dedicate our efforts to the design of hybrid TPC for wideband mmWave MIMO systems, where the beam squint dramatically affects the system performance. We firstly show that the channel matrices of the different subcarriers possess distinct subspaces in case of high bandwidths, hence traditional hybrid TPC schemes suffer from an eroded performance. Therefore, we propose novel hybrid TPC schemes exploiting the full channel state information (CSI), which project all frequencies to the central frequency and construct the common analog TPC matrix for all subcarriers. Moreover, we propose several low-complexity array-vector based hybrid TPC schemes. The high-complexity manifold optimization based hybrid TPC method and the fully digital TPC operating with and without considering beam squint are provided as benchmarks. Our extensive numerical simulations show that the proposed hybrid TPC schemes are capable of achieving similar performance to the excessive-complexity fully digital TPC, when the bandwidth tends to 0.5 GHz and always outperform the traditional hybrid TPC schemes.

This paper focuses on the design of low complexity hybrid analog/digital precoding and diversity combining in millimeter wave (mmWave) Internet of Things (IoT) systems. First, by exploiting the sparseness property of the mmWave in the angular domain, we propose a spatial lobes division (SLD) to group the total paths of the mmWave channel into several spatial lobes (SLs), where the paths in each SLs form a low-rank subchannel. Second, based on the SLD operation, we propose a low complexity hybrid precoding scheme, named hybrid precoding based on SLD (HYP-SLD). Specifically, for each low-rank subchannel, we formulate the hybrid precoding design as a sparse reconstruction problem and separately maximizes the spectral efficiency. Finally, we further propose a maximum ratio combining-based diversity combining scheme, named HYP-SLD-MRC, to improve the bit error rate (BER) performance of mmWave IoT systems. Simulation results demonstrate that, the proposed HYP-SLD scheme significantly reduces the complexity of the classic orthogonal matching pursuit scheme. Moreover, the proposed HYP-SLD-MRC scheme achieves great improvement in BER performance compared with the fully digital precoding scheme.

In this paper, we propose two non-uniform quantization (NUQ) codebook-based hybrid precoding schemes for two main hybrid precoding implementations, i.e., the full-connected structure and the sub-connected structure, to reduce the feedback overhead in millimeter wave single user multiple-input multiple-output systems. Specifically, we firstly group the angles of the arrive/departures (AOAs/AODs) of the scattering paths into several spatial lobes by exploiting the sparseness property of the millimeter wave in the angular domain, which divides the total angular domain into effective spatial lobes' coverage angles and ineffective coverage angles. Then, we map the quantization bits non-uniformly to different coverage angles and construct NUQ codebooks, where high numbers of quantization bits are employed for the effective coverage angles to quantize AoAs/AoDs and zero quantization bit is employed for ineffective coverage angles. Finally, two low-complexity hybrid analog/digital precoding schemes are proposed, which utilize the NUQ codebooks. Simulation results demonstrate that the proposed two NUQ codebook-based hybrid precoding schemes achieve near-optimal spectral efficiencies and show the superiority in reducing the feedback overhead compared with the uniform quantization codebook-based works.

Hybrid precoding is essential for millimeter wave (mmWave) multiple-input multiple output (MIMO) systems due to its inherent advantage of a high gain, whilst alleviating the high cost of hardware. However, most of the existing literature considered either the narrowband or wideband single-user mmWave MIMO scenarios. Hence in this paper we focus our attention on the more challenging design of hybrid Transmit Precoding (TPC) for wideband multiuser mmWave MIMO systems by exploiting the long-term channel's covariance matrix and the angle of departure (AoD) information. Specifically, in the analog TPC designed, firstly, the analog TPC matrix having an infinite angular resolution is constructed based on the channel's covariance matrix. Then, we also propose a non-uniformly spaced quantization codebook based analog TPC having finite angular resolution. Furthermore, a phase compensation operation is carried out to alleviate the effect of beam squint. As for the design of the digital TPC, a two-stage scheme is proposed to cancel the inter-user interference and to attain multiplexing gains. We study the effects of various parameters on the achievable sum rate and demonstrate with the aid of our simulation results that the proposed hybrid TPC is capable of achieving a similar performance to the excessive-complexity fully digital TPC.