Downlink Channel Research Articles

A power method based user selection for reconfigurable intelligent surface (RIS) assisted multiuser massive MISO systems

Reconfigurable intelligent surfaces (RISs) have recently emerged as effective solutions for providing massive connectivity. However, an effective RIS user selection method that can support massive devices remains undeveloped. In this study, a power method (PM)-based RIS user selection algorithm for RIS-assisted massive multiple-input multiple-output systems is proposed. It is proven that minimizing the condition number (CN) of the aggregated downlink channel matrix results in the same performance as maximizing the sum rate. Based on this analysis, a PM-based RIS user selection algorithm that utilizes a CN as an RIS user selection metric is proposed here. Although the complexity of conventional user selection algorithm is proportional to the square of the number of base station (BS) antennas ( M 2 ), that of the proposed algorithm is linearly proportional to the number of BS antennas ( M ). Simulation results demonstrate that, under a practical RIS control link, the proposed PMUS algorithm achieves better performance and lower complexity than the conventional user selection algorithm.

Lightweight neural network based SNIP for CSI feedback in massive MIMO

AbstractCsiNet, a deep neural network framework, utilizes an autoencoder to efficiently transmit downlink channel state information (CSI) in the feedback link, which reduces the cost of feedback, and significantly improves the quality of the reconstruction. However, the model with massive parameters incurs a lot of storage space and high computational complexity, which is impractical for low‐cost and low‐power edge devices. In this work, a lightweight CsiNet based SNIP (Single‐shot Network Pruning) is implemented, which prunes the model using the gradient information of the first training epoch, eliminating both pretraining and the complex pruning schedule. Numerical simulation results show that, under the same compression rates, the method can achieve a similar or even better reconstruction effect and more effectively reduce computational complexity, compared to traditional lightweight methods.

Impact of mobility on the downlink performance of cell-free massive MIMO systems

In this paper, we analyze the achievable downlink spectral efficiency of cell-free massive multiple input multiple output (CF-mMIMO) systems, accounting for the effects of channel aging (caused by user mobility) and pilot contamination. We consider two cases, one where user equipments (UEs) rely on downlink pilots beamformed by the access points (APs) to estimate downlink channel, and another where UEs utilize statistical channel state information (CSI) for data decoding. For comparison, we also consider cellular mMIMO and derive its achievable spectral efficiency with channel aging and pilot contamination in the above two cases. Our results show that, in CF-mMIMO, downlink training is preferable over statistical CSI when the length of the data sequence is chosen optimally to maximize the spectral efficiency. In cellular mMIMO, however, either one of the two schemes may be better depending on whether user fairness or sum spectral efficiency is prioritized. Furthermore, the CF-mMIMO system generally outperforms cellular mMIMO even after accounting for the effects of channel aging and pilot contamination. Through numerical results, we illustrate the effect of various system parameters such as the maximum user velocity, uplink/downlink pilot lengths, data duration, network densification, and provide interesting insights into the key differences between cell-free and cellular mMIMO systems.

Performance of Broadcast Channel Using Hierarchical Modulation in OFDM Downlink

This paper proposes a multiple code block transmission scheme using hierarchical modulation (HM) for a broadcast channel in the orthogonal frequency division multiplexing downlink. We investigate the average bit error rate (BER) performance of two-layer HM using 16 quadrature amplitude modulation (QAM) and three-layer HM using 64QAM in multipath Rayleigh fading channels. In multiple code block transmission using HM, the basic information bits are demodulated and decoded to all users within a cell that satisfy the bit error rate (BER) requirement. Hence, we investigate non-uniform QAM constellations to find one that suppresses the loss in the average BER of the basic information bits for HM to a low level compared to that using the original constellation in which only the basic information bits are transmitted while simultaneously minimizing the loss in the average BER of the basic information bits for the secondary and tertiary information bits from the original constellations in which the information bits of the respective layers are transmitted alone. Based on the path loss equations in the Urban Macro and Rural Macro scenarios, we also investigate the maximum distance from a base station (BS) for the information bits of each layer to attain the required average received signal-to-noise power ratio that achieves the average BER of 10-3.

Delay-Doppler-Based Key Generation Using OTFS

Key generation for secure communication is challenging in high-mobility scenarios due to the low coherence time. This is further exacerbated in frequency-division duplex systems due to the non-reciprocal wireless channel in uplink and downlink. The current work leverages the symplectic Fourier transform to convert fast-varying time-frequency domain signals to slow-varying delay-Doppler (DD) domain and utilizes the wireless channel representation in the DD domain to generate shared keys. The proposed approach is evaluated in terms of key generation and mismatch rates considering correlated eavesdropper, as well as the various randomness criteria provided by the National Institute of Standards and Technology.

Angular Reciprocity-Based CCM Reconstruction for Downlink FDD Multi-Antenna Channels With Multiple Scattering Clusters

Angular reciprocity based downlink (DL) channel covariance matrix (CCM) reconstruction has the significant superiority of requiring no uplink feedback in frequency division duplex multi-antenna systems. In this realm, maximal angle of arrival estimation-based schemes show superior performance due to its noise-robust merit for environment with a dominant scattering cluster. To extend the scheme to multiple scattering clusters environments, we propose to decompose the DL CCM into the covariance matrix of each scattering cluster and then reconstruct each DL sub-covariance matrix utilizing the estimated angular reciprocal parameters. Simulation results verify the validity of the proposed scheme in a 3GPP standard based multiple scattering clusters channel model.

Spanning Tree Method for Over-the-Air Channel Calibration in 6G Cell-Free Massive MIMO

Cell-free massive multiple-input multiple-output (MIMO) is an attractive network in 6G communications that significantly increases the spectral efficiency. Operating in time-division duplex (TDD) mode, the downlink beamforming is achieved by the estimated uplink channel, which is equal to the downlink channel due to the property of channel reciprocity. However, the involvement of different radio frequency (RF) gains in transceiver antennas renders the whole channel non-reciprocal. Therefore, it is of great necessity to calibrate the bi-directional channel. In this paper, we focus on the issue of over-the-air channel calibration in cell-free system. Taking a toy scenario as an example, we examine the performance differences between the calibration methods of ‘Direct Process’ and ‘Indirect Process’. A novel low-cost calibration method based on spanning tree model is proposed specifically for this distributed AP scenario, where a calibration tree is established to calculate calibration coefficients. Numerical results manifest that higher accuracy of our method is achieved compared to the existing calibration methods in the literatures. Our method is less sensitive to the location of master AP compared to Argos, and practical applications under the impact of phase noise show the priority of our method compared to LS method.

Zero-Crossing Precoding Techniques for Channels With 1-Bit Temporal Oversampling ADCs

A promising approach to reducing the energy consumption is to consider coarse quantization at the receiver. In this study, we investigate novel precoding techniques in space and time for bandlimited multiuser MIMO downlink channels with 1-bit quantization and oversampling at the receiver, considering zero-crossing modulation. The proposed time-instance zero-crossing modulation conveys the information into the time-instances of zero-crossings. Two design criteria for time-instance zero-crossing modulation are investigated, namely, the minimum distance to the decision threshold and the mean-square error between the received and the desired signal. The maximization of the minimum distance to the decision threshold can be formulated as a quadratically constraint quadratic program. As an alternative, an equivalent problem can be formulated based on power minimization, which reduces computational complexity. Departing from the conventional mean-square error based technique, a more sophisticated algorithm is developed, which implies active constellation extension in order to improve the performance at high SNR. The extended problem is solved with two approaches, namely by formulating the problem as a second-order cone program and by considering an alternating optimization algorithm. Numerical results show that the proposed time-instance zero-crossing precoding methods significantly improve the bit error rate compared to the state-of-the-art methods.

Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning.

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input-multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder-decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink's magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.

CCA-Net: A Lightweight Network Using Criss-Cross Attention for CSI Feedback

To enhance the performance of a massive multiple-input multiple-output (MIMO) system, the downlink channel state information (CSI) should be compressed and fed back to the base station (BS) effectively in a frequency division duplex (FDD) mode. In contrast to traditional compression methods that rely on the particular prior assumptions, deep learning (DL) based CSI feedback has the potential to learn informatively from the hidden complex models behind the data. However, most of the current DL based methods stay in the early stage that uses DL modules directly from image processing, while the distinctive channel semantic information has been largely ignored. In this letter, considering the spatial-temporal correlation property of CSI matrices, we propose an architecture based on criss-cross attention, called CCA-Net, which extracts the channel features more precisely by leveraging the contextual information along the delay and angle domain. Meanwhile, a real and imaginary part combination module is used to assist in extracting the internal features of CSI. In addition, to reap the computational resources at BSs, a scalable decoder is designed to extend the reconstruction abilities. Experiments demonstrate that our network achieves better normalized mean square error with lower flops than existing state-of-the-art algorithms.

Clustered Sparse Channel Estimation for Massive MIMO Systems by Expectation Maximization-Propagation (EM-EP)

We study the problem of downlink channel estimation in multi-user massive multiple input multiple output (MIMO) systems. To this end, we consider the use of Bayesian compressive sensing approach in which the clustered sparse structure of the channel in the angular domain is employed to reduce the pilot overhead. To capture the clustered structure, we employ a conditionally independent identically distributed Bernoulli-Gaussian prior on the sparse vector representing the channel, and a Markov prior on its support vector. An expectation propagation (EP) algorithm is developed to approximate the intractable joint distribution of the sparse vector and its support with a distribution from an exponential family. The approximate distribution is then used for direct estimation of the channel. The EP algorithm assumes that the model parameters are known a priori. Since these parameters are unknown, we estimate these parameters using the expectation maximization (EM) algorithm. The combination of EM and EP referred to as EM-EP algorithm is reminiscent of the variational EM approach. Simulation results show that the proposed EM-EP algorithm outperforms several recently-proposed algorithms in the literature.

Performance of Downlink Channel Equalization in Various Modulation Mappings for Long Term Evolution Systems

Performance of Downlink Channel Equalization in Various Modulation Mappings for Long Term Evolution Systems

Downlink channel estimation of intelligent reflective surface aided MU-MIMO system

Obtaining channel status information (CSI) is one of the main problems of the intelligent reflective surface (IRS) assisted communication system. This is because IRS has only a large number of passive reflective components and cannot process signals. In this paper, we aim to estimate downlink channel of IRS assisted multi-user multiple-input multiple-output (MU-MIMO) communication system. In time division duplex (TDD) mode, the downlink channel is estimated according to the uplink pilot by using channel reciprocity. We propose a parallel factor analysis based on projection subspace (PSS-PARAFAC) channel estimation scheme. In the first step, the common sparsity among multiple users is used for signal subspace projection to reduce noise energy and complexity. The second step is to convert each user signal received of base station (BS) into the PARAFAC problem. The iterative method of alternating least squares (ALS) is used to solve this problem. From the analysis of simulation results, the channel estimation performance of the proposed algorithm is better.

Intelligent Omni-Surfaces Aided Wireless Communications: Does the Reciprocity Hold?

Intelligent omni-surfaces (IOS) have attracted great attention recently due to its potential to achieve full-dimensional communications by simultaneously reflecting and refracting signals toward both sides of the surface. However, it still remains an open question whether the reciprocity holds between the uplink and downlink channels in the IOS-aided wireless communications. In this work, we first present a physics-compliant IOS-related channel model, based on which the channel reciprocity is investigated. We then demonstrate the angle-dependent electromagnetic response of the IOS element in terms of both incident and departure angles. This serves as the key feature of IOS that drives our analytical results on beam non-reciprocity. Finally, simulation and experimental results are provided to verify our theoretical analyses.

Adversarial training-aided time-varying channel prediction for TDD/FDD systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network (CPcGAN) is proposed for time division duplexing/frequency division duplexing (TDD/FDD) systems. CPcGAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information (CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI. The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

Fully-Decoupled Radio Access Networks: A Flexible Downlink Multi-Connectivity and Dynamic Resource Cooperation Framework

To enable flexible base stations (BS) association and dynamic resource management for personalized user equipment (UE) download service provision in the next-generation mobile communication network (6G), in this paper, we investigate the downlink (DL) transmission scenario in an origin fully-decoupled radio access network (FD-RAN) architecture. Considering the unique fully-decoupled UL/DL access feature, we propose an efficient two-stage DL channel estimation method in the FD-RAN. We formulate a novel multi-connectivity and dynamic resource cooperation problem with joint multiple-BS and multiple-UE association and coordinated beamforming, aiming at maximizing the weighted sum achievable rate in DL FD-RAN. By leveraging the many-to-many swap-matching theory and fractional relaxation approach, we solve the dynamic UE scheduling problem with multiple-BS and multiple-UE association and the coordinated beamforming problem, respectively. Extensive simulation results based on standard 3GPP 36.873 urban micro channel demonstrate that the proposed framework can improve the average spectral efficiency by 34.9% as compared to the traditional maximum ratio transmission beamforming method.

Full-duplex multi-user MIMO communication systems performance optimization using leakage-based precoding

The spectral efficiency (SE) can approximately double when using full-duplex (FD) multiuser MIMO communications. However, there are difficulties because of multiuser interferences, self-interference (SI), and co-channel interference (CCI). To improve the SE of the downlink (DL), this paper proposes CCI-aware enhancement to SLNR (signal-to-leakage-and-noise-ratio) signal-to-leakage-and-noise-ratio (SLNR). It considers a suppressing filter at the receiver to cancel the interferences again designing a beamformer based on CCI-plus-noise covariance matrices for every user at the transmitting side. Additionally, we propose an improvement in the SLNR method by using SI-plus-noise covariance matrices to design uplink (UL) beamformers. Unlike zero-forcing and block-diagonalization, the SLNR approach serves numerous antennas at users and BS (base station). The total SE of the communication yielded using the optimized precoder, i.e., obtained from the SLNR-based precoding. To achieve maximum energy efficiency (EE), we use a power consumption model. Simulation results confirm that full-duplex performs well compared to half-duplex (HD) when the number of antennas at every user in uplink as well downlink channels grow, for all Rician factors, for slight powers of the CCI and SI, and a limited number of antennas at the BS. With the proposed scheme for given transmit power and circuit power, we demonstrate that FD has a higher EE than HD.

Two Stage Beamforming in Massive MIMO: A Combinatorial Multi-Armed Bandit Based Approach

In frequency division duplex (FDD) massive multi-input multi-output (MIMO), the two-stage beamforming (TSB) using channel covariance matrices (CCM) can significantly reduce the downlink training length (DTL) and channel feedback. However, the overhead to estimate the CCM is large. In this paper, a combinatorial multi-armed bandit (CMAB) based TSB scheme is proposed without requirement of CMM. Particularly, the problem of the pre-beamforming matrix design is transformed into a CMAB problem. We consider the pre-beamforming matrix design in each slot as the arm selection in the CMAB, and convert the problem of the arm selection into a 0-1 integer linear programming problem, which can be solved by the branch-and-bound method. During the training process, the maximum likelihood method is used to detect the power of angle spectrum, and the angle range of each user is determined adaptively. We prove that the regret grows logarithmically with time, such that the proposed scheme converges towards the optimal action. Finally, simulation results demonstrate that the proposed scheme can significantly improve the spectral efficiency.

Downlink Interference Analysis of UAV-Based mmWave Fronthaul for Small Cell Networks

In this paper, an unmanned aerial vehicles (UAV)-based heterogeneous network is studied to solve the problem of transferring massive traffic of distributed small cells to the core network. First, a detailed three-dimensional (3D) model of the downlink channel is characterized by taking into account the real antenna pattern, UAVs' vibrations, random distribution of small cell base stations (SBSs), and the position of UAVs in 3D space. Then, a rigorous analysis of interference is performed for two types of interference: intra-cell interference and inter-cell interference. The interference analysis results are then used to derive an upper bound of outage probability on the considered system. Using numerical results show that the analytical and simulation results match one another. The results show that, in the presence of UAV's fluctuations, optimizing radiation pattern shape requires balancing an inherent tradeoff between increasing pattern gain to reduce the interference as well as to compensate large path loss at mmWave frequencies and decreasing it to alleviate the adverse effect of a UAV's vibrations.

Machine Learning-Based CSI Feedback With Variable Length in FDD Massive MIMO

To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division duplex (FDD) systems, the CSI is acquired through a feedback signal from the user equipment (UE). However, this may lead to an important overhead in FDD massive MIMO systems. Focusing on these systems, in this study, we propose a novel strategy to design the CSI feedback. Our strategy allows to optimally design variable length feedback, that is promising compared to fixed feedback since users experience channel matrices differently sparse. Specifically, principal component analysis (PCA) is used to compress the channel into a latent space with adaptive dimensionality. To quantize this compressed channel, the feedback bits are smartly allocated to the latent space dimensions by minimizing the normalized mean squared error (NMSE) distortion. Finally, the quantization codebook is determined with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering. Numerical simulations show that our strategy improves the zero-forcing beamforming sum rate by 17%, compared to CsiNetPro. The number of model parameters is reduced by 23.4 times, thus causing a significantly smaller offloading overhead. At the same time, PCA is characterized by a lightweight unsupervised training, requiring eight times fewer training samples than CsiNetPro.