Channel State Information Overhead Research Articles

An efficient parallel self-attention transformer for CSI feedback

In massive multi-input multi-output (MIMO) systems, it is necessary for user equipment (UE) to transmit downlink channel state information (CSI) back to the base station (BS). As the number of antennas increases, the feedback overhead of CSI consumes a significant amount of uplink bandwidth resources. To minimize the bandwidth overhead, we propose an efficient parallel attention transformer, called EPAformer, a lightweight network that utilizes the transformer architecture and efficient parallel self-attention (EPSA) for CSI feedback tasks. The EPSA expands the attention area of each token within the transformer block effectively by dividing multiple heads into parallel groups and conducting self-attention in horizontal and vertical stripes. The proposed EPSA achieves better feature compression and reconstruction. The simulation results display that the EPAformer surpasses previous deep learning-based approaches in terms of reconstruction performance and complexity.

Integrated Deep Implicit CSI Feedback and Beamforming Design for FDD mmWave Massive MIMO Systems

The heavy overhead of channel state information (CSI) feedback and the non-convexity of the hybrid beamforming (HYB) design pose great challenges for frequency-division-duplex (FDD) millimeter wave (mmWave) massive MIMO systems. In this letter, a deep HYB approach is proposed with limited and implicit CSI feedback. Specifically, we develop an autoencoder-based integrated deep implicit CSI feedback and beamforming neural network (CsiBFNet), which jointly conducts limited CSI feedback in the encoder(s) distributedly deployed on the user side and designs low-complexity beamformer in the decoder at the BS without explicit CSI reconstruction. The CsiBFNet aims to improve the achievable rate of the system rather than the CSI recovery accuracy. Simulations show that the proposed scheme outperforms the benchmark methods at the same feedback dimension and achieves 80% of the sum-rate of the existing state-of-the-art traditional HYB methods with perfect CSI even under severe compression rates (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu =32$ </tex-math></inline-formula> ).

A Novel Approach Using Convolutional Transformer for Massive MIMO CSI Feedback

In frequency division duplex model, the massive multiple-input multiple-output (MIMO) systems rely on feedback of channel state information (CSI) to perform precoding operations. This can increase the potential transmission gain of the system. The use of large scale antennas increases the feedback overhead of CSI exponentially. Therefore, CSI must be compressed for feedback. However, convolution based feedback methods lack long-range dependency modeling of the CSI, resulting in limited effects at high compression ratios. In this letter, we propose a novel neural network based on the convolutional transformer architecture to improve the performance of CSI compression and reconstruction at high compression rates. The experimental results show that the average accuracy of ours is improved by 5.39&#x0025; over the state-of-the-art method at a high compression rate of 1/64. At the same time, the overall performance of the system has been improved.

Adaptive Lightweight CNN-Based CSI Feedback for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for a user equipment (UE) to achieve a high data rate. However, massive MIMO requires channel state information (CSI) at the transmitter and the CSI overhead fed back by UEs exponentially increases as the number of antennas increases. In the last years, many studies have been conducted to solve the problem of enormous CSI feedback overhead by utilizing deep learning. In this letter, we propose an adaptive lightweight convolutional neural network (CNN) in the deep learning-based MIMO CSI feedback. The proposed network adaptively finds the compression ratio to be used in the network and reduces the computational complexity of the network. Simulation results show that the proposed lightweight CNN significantly reduces the computational complexity in comparison with the conventional CsiNet while achieving the equivalent performance; and moreover the proposed network converges faster.

A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems

This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering. The slow time-varying CSI between the base station (BS) and feasible UT locations in the service area is calculated from the geospatial data by offline ray tracing and a DNN-based path estimation model associated with the 1-dimensional convolutional neural network (1D-CNN) and regression tree ensembles. Then, the UT-level CSI of all feasible locations is grouped into clusters by a proposed FCM clustering. Finally, the service area is divided into a number of non-overlapping UT zones. Each UT zone is characterized by a corresponding set of clusters named as UT-group CSI, which is utilized in the analog RF beamformer design of AB-HP to reduce the required large online CSI overhead in the MU-mMIMO systems. Then, the reduced-size online CSI is employed in the baseband (BB) precoder of AB-HP. Simulations are conducted in the indoor scenario at 28 GHz and tested in an AB-HP MU-mMIMO system with a uniform rectangular array (URA) having 16x16=256 antennas and 22 RF chains. Illustrative results indicate that 91.4% online CSI can be reduced by using the proposed offline channel estimation technique as compared to the conventional online channel sounding. The proposed DNN-based path estimation technique produces same amount of UT-level CSI with runtime reduced by 65.8% as compared to the computationally expensive ray tracing.

Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to achieve spatial diversity and multiplexing gains. In a frequency division duplex (FDD) multiuser massive MIMO network, each user needs to compress and feedback its downlink CSI to the BS. The CSI overhead scales with the number of antennas, users and subcarriers, and becomes a major bottleneck for the overall spectral efficiency. In this paper, we propose a deep learning (DL)-based CSI compression scheme, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepCMC</i> , composed of convolutional layers followed by quantization and entropy coding blocks. In comparison with previous DL-based CSI reduction structures, DeepCMC proposes a novel fully-convolutional neural network (NN) architecture, with residual layers at the decoder, and incorporates quantization and entropy coding blocks into its design. DeepCMC is trained to minimize a weighted rate-distortion cost, which enables a trade-off between the CSI quality and its feedback overhead. Simulation results demonstrate that DeepCMC outperforms the state of the art CSI compression schemes in terms of the reconstruction quality of CSI for the same compression rate. We also propose a distributed version of DeepCMC for a multi-user MIMO scenario to encode and reconstruct the CSI from multiple users in a distributed manner. Distributed DeepCMC not only utilizes the inherent CSI structures of a single MIMO user for compression, but also benefits from the correlations among the channel matrices of nearby users to further improve the performance in comparison with DeepCMC. We also propose a reduced-complexity training method for distributed DeepCMC, allowing to scale it to multiple users, and suggest a cluster-based distributed DeepCMC approach for practical implementation.

Reconfigurable Intelligent Surface Assisted D2D Underlay Communications: A Two-Timescale Optimization Design

The performance of a device-to-device (D2D) underlay communication system is limited by the co-channel interference between cellular users (CUs) and D2D devices. To address this challenge, a reconfigurable intelligent surface (RIS) aided D2D underlay system is studied in this paper. A two-timescale optimization scheme is proposed to reduce the required channel training and feedback overhead, where transmit beamforming at the base station (BS) and power control at the D2D transmitters are adapted to instantaneous effective channel state information (CSI); and the RIS phase shifts are adapted to slow-varying channel mean. Based on the two-timescale optimization scheme, we aim to maximize the D2D ergodic weighted sum-rate (WSR) subject to a given outage probability constrained signal-to-interference-plus-noise ratio (SINR) target for each CU. The two-timescale problem is decoupled into two sub-problems, and the two sub-problems are solved iteratively. Numerical results verified that the two-timescale based optimization performs better than two baselines, and also demonstrated a favourable trade-off between system performance and CSI overhead.

Massive MIMO CSI reconstruction using CNN‐LSTM and attention mechanism

In massive multiple-input multiple-output (MIMO) systems, the channel state information (CSI) feedback enables performance gain in frequency division duplex networks. However, with the increase in the number of antennas, the feedback overhead of CSI will also enhance. To this end, this study addresses the issue of massive MIMO CSI reconstruction using convolutional neural network (CNN), long short-term memory (LSTM) and attention mechanism, and proposes an efficient network architecture (denoted as CNN-LSTM-A). To achieve a compromise between performance and complexity, the proposed method significantly reduces the number of training parameters by utilising a single-stage network rather than a multiple-stage network. Finally, simulation results show that the authors method can reduce the feedback overhead of CSI effectively, and achieves better performance in terms of CSI compression and recovery accuracy compared with existing state-of-the-art methods.

AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems

Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith. By considering the noisy CSI due to imperfect channel estimation, we propose a novel deep neural network architecture, namely AnciNet, to conduct the CSI feedback with limited bandwidth. AnciNet extracts noise-free features from the noisy CSI samples to achieve effective CSI compression for the feedback. Experimental results verify that the proposed AnciNet approach outperforms the existing techniques under various conditions.

Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks.

For massive multiple-input multiple-output (MIMO) distributed wireless sensor networks, this paper investigates the role of multi-antenna sensors in improving network perception performance. First, we construct a distributed multi-antenna sensor network based on massive MIMO. By using the anti-fading characteristics of multi-antennas, it is better to achieve accurate detection than the single-antenna sensor network. Based on this, we derive a closed-loop expression for the detection probability of the best detector. Then, we consider the case that the sensor power resources are limited, and thus we want to use finite power to achieve higher detection probability. For this reason, the power was optimized by the alternating direction method of multipliers (ADMM). Moreover, we also prove that only statistical channel state is needed in large-scale antenna scenarios, which avoid the huge overhead of channel state information. Finally, according to the simulation results, the multi-antenna sensor network has better detection performance than the single-antenna sensor network which demonstrates the improved performance of the proposed schemes and also validates the theoretical findings.

User-Centric Joint Access-Backhaul Design for Full-Duplex Self-Backhauled Wireless Networks

Full-duplex self-backhauling is promising to provide cost-effective and flexible backhaul connectivity for ultra-dense wireless networks, but also poses a great challenge to resource management between the access and backhaul links. In this paper, we propose a user-centric joint access-backhaul transmission framework for full-duplex self-backhauled wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by multiple small base stations (SBSs). In the backhaul link, user-centric multicast transmission is proposed so that each user’s message is treated as a common message and multicast to its serving SBS cluster. We first formulate an optimization problem to maximize the network weighted sum rate through joint access-backhaul beamforming and SBS clustering when global channel state information (CSI) is available. This problem is efficiently solved via the successive lower-bound maximization approach with a novel approximate objective function and the iterative link removal technique. We then extend the study to the stochastic joint access-backhaul beamforming optimization with partial CSI. Simulation results demonstrate the effectiveness of the proposed algorithms for both full CSI and partial CSI scenarios. They also show that the transmission design with partial CSI can greatly reduce the CSI overhead with little performance degradation.

Randomized Two-Timescale Hybrid Precoding for Downlink Multicell Massive MIMO Systems

Although massive multiple-input multiple-output (MIMO) promises high spectral efficiency, there are several issues that significantly limit the potential gain of massive MIMO, such as severe inter-cell interference, huge channel state information (CSI) overhead/delay, high cost and power consumption of RF chains, and user fairness. Several precoding schemes have been proposed for massive MIMO but there still lacks a systematic solution to address all of the aforementioned issues especially for the multicell case. In this paper, we propose a randomized two-timescale hybrid precoding (RTHP) scheme for the downlink transmission of multicell massive MIMO systems. RTHP allows time sharing among multiple sets of analog precoders to achieve better tradeoff between sum throughput and fairness. Moreover, the time-sharing factors and analog precoders are adapted to the long-term channel statistics to mitigate inter-cell interference with reduced CSI overhead and RF chains. The digital precoder is used to support multiuser MIMO at each base station based on local low-dimensional effective CSI only. As such, RTHP resolves all of the aforementioned issues. We formulate the optimization of RTHP as a general network utility maximization problem (GNUMP), which is a two-timescale stochastic non-convex problem, and consequently challenging to solve. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop an online algorithm, which is shown to converge to stationary solutions of the approximate GNUMP. Finally, simulation results verify the advantages of the proposed scheme over baseline schemes.

Deep Clustering-Based Codebook Design for Massive MIMO Systems

Deep learning (DL) can effectively handle explosive data and solve the complicated non-convex problems. Recently, it is used to reduce the overhead of channel state information (CSI) feedback and improve the performance of the limited feedback massive MIMO systems. However, the complex wireless environment and the high-dimensional data of massive MIMO channel degrades the performance of the DL based codebook design. To address this issue, we propose a deep clustering (DC) based codebook design for massive MIMO systems. At first, deep neural network (DNN) learns the key propagation characteristics of the wireless channel according to the training model. And then, the clustering algorithm outputs the centroids of each key propagation characteristic, which can be used to reconstruct the valid space of massive MIMO channel. Finally, massive MIMO codebook is constructed by the valid space. Since the amount of the key propagation characteristics are far less than the dimension of massive MIMO channel, the proposed design can significantly simplify the parameters and the structures of DNN. Moreover, the clustering algorithm output the information with minimum sum-distance with the real channel sequences which can acquire the maximum sum-rate of massive MIMO codebook design. The robustness of the proposed codebook design is validated by carrying out extensive simulation in various scenarios and antenna array structures. Simulation results consist with theoretical analysis in terms of the achievable rate, and demonstrate that the proposed codebook design outperforms the traditional schemes.

Low-Overhead Feedback Topology Design for the K-User MIMO Interference Alignment

Since designing a feedback topology is a practical way to implement interference alignment at reduced cost of channel state information (CSI) feedback, six feedback topologies have been presented in prior works for a K-user multiple-input multiple-output interference channel. To fully reveal the potential benefits of the feedback topology in terms of the saving of CSI overhead, we propose a new feedback topology in this paper. By efficiently performing dimensionality-decreasing at the transmitter side and aligning interference signals at a subset of receivers, we show that the proposed feedback topology obtains substantial reduction of feedback cost over the existing six feedback designs under the same antenna configuration.

Dynamic Asymmetric Power Splitting Scheme for SWIPT-Based Two-Way Multiplicative AF Relaying

Power splitting (PS) scheme design is one of the most important challenges in simultaneous wireless information and power transfer-based two-way multiplicative amplify-and-forward relay networks. In this letter, we propose a novel dynamic asymmetric PS (DAPS) scheme to minimize the system outage probability by exploiting the asymmetric instantaneous channel gains between the relay node and the destination nodes. As the formulated optimization problem is a nonconvex problem and difficult to solve, we reformulate it as a fractional programming problem and propose a Dinkelbach-based iterative algorithm to obtain the optimal asymmetric PS ratios. Both the analytical and simulation results demonstrate that the proposed DAPS scheme with the same channel state information overhead can significantly reduce the system outage probability as compared with the existing static symmetric PS scheme.

Low‐latency and high‐reliability performance analysis of relay systems

The latency and reliability of single-relay and multi-relay systems are investigated in this study. In a single-relay system with the amplify-and-forward protocol, a new approximation analytical method is proposed for the outage probability, which can also be extended to be used in the multi-relay system. The latency-reliability trade-off degree (LRTD) of single- and multi-relay systems, i.e. the slope of the latency-outage probability curve with logarithmic scales, is analysed and proved to be the same as the number of relay nodes. In the multi-relay system, the relationship between the system LRTD and the channel state information overhead is investigated. The optimum number of relay nodes can be derived according to the approximate expression of the outage probability. A simple iterative method is proposed to obtain the optimum number of relay nodes. The convergence of the iterative method is also proved.

QoS-based Joint User Selection and Scheduling for MU-MIMO WLANs

The shift in Multi-User Multiple Input Multiple Output (MU-MIMO) has gained attention due to its wide support in very high throughput Wireless Local Area Networks (WLANs) such as the 802.11ac. However, the full advantage of MU-MIMO can be utilized only with proper user selection and scheduling. Also, providing Quality of Service (QoS) support is a major challenge for these wireless networks. Generally, user scheduling is done with the acquisition of Channel State Information (CSI) from all the users. In MU-MIMO based WLANs, the number of CSI request increases with the number of users. This results in an increased CSI overhead and in degradation of the overall throughput. Most of the proposals in the literature have not addressed the contention in the CSI feedback clearly. Hence, in this paper a Joint User Selection and Scheduling (JUSS) scheme is discussed and its performance is evaluated in terms of throughput, delay, packet loss and fairness. In the performance comparison some wellknown Medium Access Control (MAC) protocols are considered. The proposed scheme not only enhances throughput, but also avoids contention during CSI feedback period.

Adaptive Time-Switching Based Energy Harvesting Relaying Protocols

Considering a dual-hop energy-harvesting (EH) relaying system, this paper advocates novel relaying protocols based on adaptive time-switching (TS) for amplify-and-forward and decode-and-forward modes, respectively. The optimal TS factor is first studied, which is adaptively adjusted based on the dual-hop channel state information (CSI), accumulated energy, and threshold signal-to-noise ratio (SNR), to achieve the maximum throughput efficiency per block. To reduce the CSI overhead at the EH relay, a low-complexity TS factor design is presented, which only needs single-hop CSI to determine the TS factor. Theoretical results show that, in comparison with the conventional solutions, the proposed optimal/low-complexity TS factor can achieve higher limiting throughput efficiency for sufficiently small threshold SNR. As the threshold SNR approaches infinity, the throughput efficiency of the proposed optimal/low-complexity TS factor tends to zero in a much slower pace than that of the conventional solutions. Simulation results are presented to corroborate the proposed methodology.

New Beamforming Designs for Joint Spatial Division and Multiplexing in Large-Scale MISO Multi-User Systems

In this paper, we study a joint spatial division multiplexing (JSDM) beamforming scheme, which enables large-scale spatial multiplexing gains for massive multi-input multi-output downlink systems. In contrast to the conventional JSDM, which employs a block diagonalization method as a pre-beamformer, we aim to maximize sum-rate by applying minimum-mean-squared error (MMSE) approaches when designing a pre-beamformer and a multi-user precoder sequentially. First, to suppress inter-group interference, we design the pre-beamformer, which minimizes an upper bound of the sum mean-squared-error in the large-scale array regime. Then, to mitigate same-group interference, we present the multi-user precoder based on the weighted MMSE (WMMSE) optimization method, which requires the same channel state information overhead as the conventional JSDM. Also, in order to reduce the computational complexity, we compute deterministic equivalents of the WMMSE beamforming parameters to generate the beamformers by employing asymptotic results of large system analysis. Through simulation results, we confirm that the proposed two-step beamforming methods bring substantial performance gains in terms of sum-rate over the conventional JSDM schemes especially in a low and medium signal-to-noise ratio regime with comparable complexity.

Average effective degrees of freedom (AEDoF) maximization with interference alignment in small cell networks

The emergence of small cells provides a cost-effective way to satisfy users’ explosive traffic requirements. The massive deployment of small cells, nevertheless, causes severe inter-cell interference in Orthogonal Frequency Division Multiple-Access -based cellular networks. As such, conventional interference management strategies may be inefficient and interference alignment (IA) has been proposed as a promising technology to cope with inter-cell interference. To perfectly align all interference in a reduced-dimensional subspace, IA transmitters generally call for global channel state information (CSI) across small cell networks through receivers’ feedback. However, the number of total feedback bits scales as the square of the number of small cells. Hence, IA achieves a greater multiplexing gain at the cost of substantial overhead. To enable a tradeoff between multiplexing gain and overhead reduction, in this paper we present a new metric termed average effective degrees of freedom (AEDoF), which embodies the average degrees of freedom of small cell networks with CSI overhead considered. Furthermore, for reducing the computational complexity, we propose a graph-based clustering algorithm to solve the formulated AEDoF maximization problem. Simulation results verify that our proposed algorithm is of low complexity and achieves the maximum spectrum efficiency among several existing clustering methods.