The fifth-generation (5G) wireless communication signal is ideal for positioning by the user equipment (UE) due to its high precision, low cost, low latency, and easy integration. However, UE-side positioning faces significant challenges in complex electromagnetic environments. Obstacles that block the line-of-sight (LOS) path can severely impact localization accuracy. This paper proposes a deep learning architecture with two-stage inference to achieve high-precision positioning at the user side with multipath channels. For the first time, online learning is achieved without the need to retrain the neural network (NN), making it suitable for implementation on UE. Specifically, the variational inference theory is used to sense the environment and improve positioning accuracy using multipath information. This approach significantly reduces range error in non-line-of-sight (NLOS) channels. Furthermore, a new NN with a neural processes regressor (NPR) is proposed to learn the distribution of ranging bias directly from received signals. The proposed learning architecture and networks can be implemented without retraining, even under different circumstances and environments, making it more computationally efficient than most existing NNs and suitable for practical deployment. Simulation results demonstrate that the proposed approach outperforms conventional techniques with various channels.

Millimeter wave (mmWave) communication stands as a crucial technology for X-reality and cloud gaming due to its extensive bandwidth. Nevertheless, its user experience suffers from degradation caused by obstacles like humans and objects. Unlike base station (BS) solutions, user-side approaches offer greater flexibility in addressing blockage issues. This paper introduces an innovative beam management architecture named reference-signal-received-power (RSRP)-based blockage-prediction-aided compressive sensing (RSRP-BPCS). Unlike existing methods, our proposed algorithm accounts for real communication system constraints and protocol procedures, ensuring practicality. The key advantage lies in deriving channel information based on RSRP without requiring signal phase knowledge. Additionally, we present two novel strategies on the user side: a multiple-beam-sweeping procedure (MBSP) and a proactive blockage prediction approach. These techniques enable the monitoring of different beam pair links and early anticipation of blockage instances. To achieve rapid recovery, we design an explainable deep learning beam tracking scheme which is joint data and model-based to explore the compressive projection of the power spectrum. The best part is that our strategies are standard-compatible, demanding no supplementary side information, which significantly simplifies implementation with minimal complexity. Through extensive simulations, our proposed algorithm demonstrates exceptional performance in mitigating the impact of blockage, proving its efficacy and practicality.

Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The channel sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated on discrete angles of arrival/departure. In addition, these dictionaries are assumed to be the same for all subcarriers, without considering the impacts of hardware impairments and beam squint. In this manuscript, we derive a general channel and signal model that explicitly incorporates the impacts of hardware impairments, practical pulse shaping functions, and beam squint, overcoming the limitations of mmWave MIMO channel and signal models commonly used in previous work. Then, we propose a dictionary learning (DL) algorithm to obtain the sparsifying dictionaries embedding hardware impairments, by considering the effect of beam squint without introducing it into the learning process. We also design a novel CS channel estimation algorithm under beam squint and hardware impairments, where the channel structures at different subcarriers are exploited to enable channel parameter estimation with low complexity and high accuracy. Numerical results demonstrate the effectiveness of the proposed DL and channel estimation strategy when applied to realistic mmWave channels.

Beam management under blockage has been challenging for millimeter wave communication relying on directional links. The baseline beam management protocols in 5G new radio (NR) are not able to predict and avoid the beam blockage timely, leading to significant link quality degradation and beam switching delays. In this paper, we propose a new dual- beam-sweeping-procedure (DBSP) scheme as well as a proactive blockage prediction and fast beam switching strategy at the user side. Specifically, we propose to only exploit existing resources in 5G NR standards and allocate them for DBSP, i.e., the regular beam sweeping procedure in 5G NR as well as an additional serving beam pair link (BPL) monitoring procedure. The DBSP then facilitates the user-side proactive blockage prediction. Once the beam blockage instance is predicted, users could determine candidate BPL earlier for fast beam switching so that the delays of beam switching can be reduced significantly. Compared to existing works, the proposed strategies are standard-compatible and require no additional side information, like location, map, or vision, making them easier to implement and of low complexity. Moreover, the proposed user-side strategies are more flexible than the base station (BS)-side methods, especially for beam switching/handover between BSs. Numerical results on both statistical and ray-tracing blockage channel models are provided to demonstrate the superiority of the proposed algorithms.

Beamforming design in non-line-of-sight (N-LOS) multipath channel is challenging for millimeter-wave (mmWave) user equipment (UE), especially when hardware imperfections introduce random phase distortions to the received signals. In this paper, we propose a novel analog beam codebook design algorithm for UE, called approximate correlation matrix (ACM). In the proposed algorithm, beamforming vector is designed according the reference signal received power (RSRP), without the need for exact phase information of received signals. In particular, we exploit the correlation among antennas from phaseless measurements, and derive the precoding/combining vector through a simple Fourier transform. Simulation results show that the proposed algorithms can achieve near-optimal performance with low complexity.

Due to the fast deployment and commercial use of fifth generation (5G) communication systems, there is an increasing demand for higher uplink rates, and thus the deployment of more transmit antennas at user equipment (UE) becomes even more urgent. Nowadays, to better balance the uplink user experience and terminal cost, a feasible way to deploy larger number of transmit antennas at UE is to patch together multiple smaller radio frequency integrated circuits (RFICs), e.g., two RFICs of 2 transmit antennas (2T) will be used to implement 4T. However, this setup will induce a random phase difference between the two RFICs that is unknown at the transmitter. In this paper, we investigate the optimal uplink digital precoder design under such random phase imperfections. In terms of maximizing the average channel capacity, we find that the optimal precoder will be the eigen-vectors of an adjusted transmit correlation matrix. Block-diagonal precoders are also shown to be robust to random phase errors at the expense of some loss in degrees of freedom and channel capacity. Numerical simulations are provided to verify the effectiveness of the proposed uplink precoders under random phase impacts.

Compressive channel estimation is an effective way to reduce the number of measurements for fast beam tracking in millimeter wave (mmWave) communications. However in some practical scenarios, the phase of the received signal is difficult to measure, leading to challenges for fast beam tracking, especially in non-line-of-sight (NLoS) channels. In this paper, we propose a novel beam tracking algorithm under random phase offset scenarios using compressive sensing (CS). Unlike traditional algorithms based on complex signal measurements, the proposed algorithm could derive the channel information using reference signal received power (RSRP), without the need of knowledge of the phase. To recover the compressive channel with high precision and low complexity, we design a deep learning beam tracking scheme utilizing a complex-valued auto-encoder. Simulation results show that the proposed scheme can outperform traditional hierarchical search manner under blockage and rotation scenarios.

Tracking wideband millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems based on a hybrid architecture is a challenging problem, especially in high mobility scenarios where links are likely to be blocked by obstacles, such as trees, pedestrians or vehicles. In this paper, we propose a new strategy to track the frequency selective mmWave channel under blockage. We first introduce a statistical channel model that includes the evolution models for channel gains and angles of arrival and departure, as well as the statistics of blockage events. Then, we define a change point detection (CPD) test to identify the time instants where blockage appears/disappears, so the appropriate channel evolution models can be used for wideband channel tracking during the blockage events. To simultaneously achieve a high CPD success rate and a high accuracy in the channel estimate, we further propose a double digital combiner architecture that employs different digital combiners for CPD and channel tracking. Finally, we integrate into our framework a previously proposed Bayesian channel tracking algorithm. Simulation results show that the proposed approach achieves a good channel tracking performance even in mobile scenarios that suffer from highly dynamic blockage events.

A major source of difficulty when operating with large arrays at millimeter wave (mmWave) frequencies is to estimate the wideband channel, since the use of hybrid architectures acts as a compression stage for the received signal. Moreover, the channel has to be tracked and the antenna arrays regularly reconfigured to obtain appropriate beamforming gains when a mobile setting is considered. In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms. One of them exploits the sparsity of the mmWave channel, while the other one leverages prior statistical information about the channel parameters. We also propose a hybrid precoder and combiner design method to increase the received signal-to-noise ratio (SNR) during channel tracking, such that near-optimum data rates can be obtained with low-overhead. In our numerical results, we analyze the performance of our proposed algorithms for different system parameters. Simulation results show that our proposed channel tracking algorithms are able to achieve near-optimum data rates outperforming state-of-the-art methods.

Exploiting channel sparsity at millimeter wave (mmWave) frequencies reduces the high training overhead associated with the channel estimation stage. Compressive sensing (CS) channel estimation techniques usually adopt the (overcomplete) Fourier transform matrix as sparsifying dictionary. This may not be the best choice when considering hardware impairments in practical arrays. We propose two dictionary learning (DL) algorithms to learn the best sparsifying dictionaries for channel matrices from observations obtained with practical hybrid frequency-selective mmWave multiple-input-multiple-output (MIMO) systems. First, we optimize the combined dictionary, i.e., the Kronecker product of transmit and receive dictionaries, as it is used in practice to sparsify the channel matrix. This stage operates as a calibration phase, since all the hardware imperfections are embedded into the learnt dictionaries. Second, considering the different array structures at the transmitter and receiver, we exploit separable DL to find the best transmit and receive dictionaries. Once the channel is expressed in terms of the optimized dictionaries, various CS-based sparse recovery techniques can be applied for low overhead channel estimation. The effectiveness of the proposed DL algorithms under low SNR conditions has been corroborated via numerical simulations with different system configurations, array geometries and hardware impairments.

In this paper, we propose a new channel estimation scheme for TDD/FDD massive MIMO systems by reconstructing (sometimes also referred to as covariance computing or covariance fitting) uplink/downlink channel covariance matrices (CCMs) with the aid of array signal processing techniques. Specifically, the angle parameters and power angular spectrum (PAS) of channel are extracted from the instantaneous uplink channel state information (CSI). Then, the uplink CCM is reconstructed and can be used to improve the uplink channel estimation without any additional training cost. By virtue of angle reciprocity as well as PAS reciprocity between uplink and downlink channels, the downlink CCM could also be inferred with a similar approach even for the FDD massive MIMO systems. Then, the downlink instantaneous CSI can be obtained by training toward the dominant eigen-directions of each user. The proposed strategy is applicable to various PAS distributions. Numerical results are provided to demonstrate the superiority of the proposed methods over the existing ones.

In this paper, we design a channel tracking method for massive multiple-input multiple-output systems under both time-varying and spatial-varying circumstances. By exploiting the characteristics of massive antenna array, a spatial-temporal basis expansion model is proposed to reduce the effective dimension of uplink/downlink channel, which decomposes channel state information into time-varying spatial information and gain information. We first model the user's movement as the one-order unknown Markov process, whose parameters are blindly obtained by expectation and maximization learning. Then, the uplink time-varying spatial information can also be blindly tracked by unscented Kalman filter and Taylor series expansion of the steering vector, while the rest of uplink channel gain information can be trained by only a few pilot symbols. Due to physical angle reciprocity, the spatial information of the downlink channel can be immediately computed from the uplink counterpart, which greatly reduces the complexity of downlink channel tracking. Various numerical results are provided to demonstrate the effectiveness of the proposed method.

This paper proposes a new channel estimation scheme for the multiuser massive multiple-input multiple-output (MIMO) systems in time-varying environment. We introduce a discrete Fourier transform (DFT) aided spatial-temporal basis expansion model (ST-BEM) to reduce the effective dimensions of uplink/downlink channels, such that training overhead and feedback cost could be greatly decreased. The newly proposed ST-BEM is suitable for both time division duplex (TDD) systems and frequency division duplex (FDD) systems thanks to the angle reciprocity, and can be efficiently deployed by fast Fourier transform (FFT). Various numerical results have corroborated the proposed studies.

This paper proposes a novel channel estimation scheme for the multiuser massive multiple-input multiple-output (MIMO) systems. A discrete Fourier transform (DFT) aided spatial basis expansion model (SBEM) is first introduced to represent the uplink (UL)/downlink (DL) channels with much few parameter dimensions by exploiting the physical characteristics of the uniform linear array (ULA). With SBEM, pilot contamination in the UL training can be relieved by user scheduling exploiting their spatial information. Moreover, the UL spatial information can help to simplify the DL training based on the angle reciprocity. Compared to existing low-rank models, the newly proposed SBEM does not need any information of channel statistics and is suitable for both time division duplex (TDD) and frequency division duplex (FDD) systems. Moreover, the proposed method can be efficiently deployed by the fast Fourier transform (FFT) followed by linear estimator. Various numerical results are provided to corroborate the proposed studies.

In this paper, we introduce a new spatial spectrum-sharing strategy for massive multiple-input multiple-output (MIMO) cognitive radio (CR) systems. Different from the conventional MIMO CR system, CR terminals can be discriminated by their angular information with the help of high spatial resolution of massive antennas at CR base station (CBS). Moreover, the discrete Fourier transform can be applied to efficiently obtain such angular information thanks to the massive antennas, again. We then formulate a 2-D spatial basis expansion model to represent the uplink/downlink channels of CRs with reduced parameter dimensions, which immediately alleviates the general headaches of massive MIMO systems, such as uplink pilot contamination and downlink training overhead. Moreover, we present a full-space coverage concept by employing two CBSs at the adjacent sides of each cell, which diminishes the sheltering effect from the primary radio. We also design two greedy CR scheduling algorithms for the dual CBSs to improve the spectral efficiency and enhance the scheduling probability of CRs. Since the proposed strategy exploits angular information and since the angle reciprocity holds for two frequency carriers with moderate distance, the proposed strategy is applied for both time division duplex and frequency division duplex systems.