Full Channel State Information Research Articles

On the Sum Capacity Achieving Precoders and Their CSI Requirements for MIMO-BC

Dirty paper coding (DPC) precoders achieve the sum capacity of Gaussian MIMO broadcast channels. However, most systems employ zero-forcing (ZF) and MMSE precoders that are near-optimal at high SNR if the base station (BS) has a large number of antennas, but achievable sum rates with ZF and MMSE precoders are significantly below sum capacity otherwise. We show that capacity achieving DPC precoding requires CSI that scales only as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(M^{2})$ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> -antenna user (not full CSI as is generally assumed) irrespective of the number of BS antennas. Moreover, easily computable generalized decision feedback equalizers significantly outperform MMSE and ZF precoders.

Joint Massive MIMO CSI Estimation and Feedback via Randomized Low-Rank Approximation

The acquisitionof channel state information at transmitter (CSIT) is crucial to attain the potential benefits of the frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. Traditionally, CSI estimation at receiver and feedback to transmitter are separately designed, failing to maximize the estimation accuracy while minimizing the complexity/overhead. In this work, we propose a novel computation and communication efficient CSIT acquisition scheme, by jointly designing downlink CSI estimation and uplink feedback, which is inspired by randomized matrix approximation techniques. In the process of CSI estimation, we represent a large channel matrix with three sub-matrices by exploiting its inherent low-rank characteristic, and then estimate these small matrices separately. Our new estimator significantly reduces the computation complexity in massive MIMO CSI acquisition, whilst attaining the high accuracy. The convergence as well as the error bound of our CSI estimator is derived theoretically. In the following CSI feedback process, only partial elements of sub-matrices are reported to transmitter without further compression, and finally the full CSI is recovered accurately. This novel CSI feedback scheme, relying on the estimated CSI with special structure, greatly reduces both the communication overhead. Numerical simulations are provided to valid our joint CSI estimation and feedback scheme, which would have great potential in massive MIMO communications.

A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks

Massive MIMO is one of the leading technologies for connecting very large numbers of energy-constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that the mutual interference is minimized. Here we propose node partitioning strategies that do not require full channel state information, but rather are based on nodes’ respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realize a simple partitioning method, requiring minimal information to be collected from the nodes, and in which this information typically remains stable over the long term, thus promoting the system’s autonomy and energy efficiency.

Diagnosis of Intelligent Reflecting Surface in Millimeter-Wave Communication Systems

Intelligent reflecting surface (IRS) is a promising technology for enhancing wireless communication systems. It adaptively configures massive passive reflecting elements to control wireless channel in a desirable way. Due to hardware characteristics and deploying environments, an IRS may be subject to reflecting element blockages and failures, and hence developing diagnostic techniques is of great significance to system monitoring and maintenance. In this paper, we develop diagnostic techniques for IRS systems to locate faulty reflecting elements and retrieve failure parameters. Three cases of channel state information (CSI) availability are considered. In the first case where full CSI is available, a compressed sensing based diagnostic technique is proposed, which significantly reduces the required number of measurements. In the second case where only partial CSI is available, we jointly exploit the sparsity of the millimeter-wave channel and the failure, and adopt compressed sparse and low-rank matrix recovery algorithm to decouple channel and failure. In the third case where no CSI is available, a novel atomic norm is introduced as the sparsity-inducing norm of the cascaded channel, and the diagnosis problem is formulated as a joint sparse recovery problem. Finally, the proposed diagnostic techniques are validated through numerical simulations.

Finite Block Length Analysis of RIS-Assisted UAV-Based Multiuser IoT Communication System With Non-Linear EH

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations.

Low-to-Zero-Overhead IRS Reconfiguration: Decoupling Illumination and Channel Estimation

Most algorithms developed for the optimization of Intelligent Reflecting Surfaces (IRSs) so far require knowledge of full Channel State Information (CSI). However, the resulting acquisition overhead constitutes a major bottleneck for the realization of IRS-assisted wireless systems in practice. In contrast, in this letter, focusing on downlink transmissions from a Base Station (BS) to a Mobile User (MU) that is located in a blockage region, we propose to optimize the IRS for illumination of the area centered around the MU. Hence, the proposed design requires the estimation of the MU&#x2019;s position and not the full CSI. For a given IRS phase-shift configuration, the end-to-end BS-IRS-MU channel can then be estimated using conventional channel estimation techniques. The IRS reconfiguration overhead for the proposed scheme depends on the MU mobility as well as on how wide the coverage of the IRS illumination is. Therefore, we develop a general IRS phase-shift design, which is valid for both the near- and far-field regimes and features a parameter for tuning the size of the illumination area. Moreover, we study a special case where the IRS illuminates the entire blockage area, which implies that the IRS phase shifts do not change over time leading to zero overhead for IRS reconfiguration.

Distributed Resource Optimization for NOMA Transmission in Beamforming SATCOM

This work studies the application of nonorthogonal transmission in beamforming (BF) based forward links for next-generation satellite communication (SATCOM) with multiple gateways. With the aim of enhancing the throughput of BF SATCOM systems, the state-of-the-art nonorthogonal multiple access (NOMA) technique is exploited by serving multiple users per beam in the same time slot. In this regard, the feeder link limitations and multibeam satellite payload constraints must be considered for BF design and power allocation (PA) optimization in nonorthogonal SATCOM. To address these challenges, distributed resource optimization strategies are investigated for BF and flexible payload power resource allocation in multigateway (multi-GW) nonorthogonal SATCOM systems. Specifically, a per-feed available power-constrained BF strategy via maximization of the worst-user signal-to-leakage-and-noise ratio (SLNR) is explored with local channel state information (CSI) for a distributed operation of GWs. As an upper-bound performance limit, a centralized multilayer BF strategy is processed in a central unit with full global CSI and data sharing. After the BF direction optimization, a weighted sum-rate maximization-based (WSRM-based) power resource optimization strategy is locally applied at each GW to efficiently use the power resources for higher performance increment. The nonconvex WSRM problem, under the constraints of the practical satellite payload power budget, successful successive interference cancellation (SIC) decoding, and minimum data rate, is recast into an equivalent weighted sum-MSE minimization (WMMSE) counterpart for a tractable solution. Finally, an efficient user scheduling is designed to enable the operator to capture a substantial system-throughput gain. Accurate simulations are conducted with the near-to-real coverage area (footprints), the random distributions of users, and interference, relying on geographical locations of users. The results over a realistic simulation environment show the efficiency of our strategies.

A POMDP-Based Antenna Selection for Massive MIMO Communication

We use a partially observable Markov decision process (POMDP) framework to design an optimal antenna selection policy for downlink transmit beamforming at a multi-antenna base station (BS) equipped with only a limited number of RF chains. Assuming that the channel state evolves according to a finite-state Markov process and that only the channel coefficients which correspond to previously selected antennas, are available at the BS, we use the POMDP framework for antenna selection with the aim to maximize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-term expected downlink data rate</i> . To avoid the high computational complexity of the value iteration algorithm, we focus on the myopic policy and prove that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in the case of positively correlated two-state Markov model for the channel over each antenna, the myopic policy is optimal for antenna selection for any number of RF chains</i> . Based on this finding, for general fading channels, we propose to quantize each channel into two levels and apply the myopic policy for antenna selection. Our simulation results show that using this two-state coarse channel quantization for antenna selection results in only a small loss in performance, as compared to the antenna selection technique which uses full channel state information without quantization.

Full-Rate Space–Time Line Code for Four Receive Antennas

For multiple-input multiple-output systems with four receive antennas, conventional space–time line codes (STLCs) have supported code rates of 3/4, 4/7, and 1/2. To provide a wider range of transmission rates, we herein propose a rate-1 STLC for four-receive-antenna systems. We present an encoding method to achieve full rate and devise a simple decoding scheme that does not require full channel state information at the receiver. Assuming a large number of transmit antennas, we analyze the bit error rate performance and demonstrate that the proposed STLC can achieve full spatial diversity.

Process-and-Forward two-way relay using multiple space-time line codes

In this paper, we consider a two-way relay (TWR) network that two source nodes (SNs) with multiple antennas exchange data to each other via a relay node (RN) with multiple antennas, when full channel state information (CSI) is available at RN and no CSIs are available at SNs. In case that both SNs transmit space-time block coded multiple data streams to the RN, a new precoding method is proposed for the RN by combing block-wise processing and multiple space-time line codes (STLCs) requiring no CSI at the receiver except the channel norm. An optimal precoding matrix is derived that maximized the minimum achievable rate using a gradient-based iterative algorithm. Moreover, to reduce the design complexity, we derive the closed-form precoding matrix minimizing the total mean square error (MSE), and its theoretical performance is analyzed in terms of the per-stream MSE and detection signal-to-interference-plus-noise ratio. Through numerical simulations, the theoretical MSE is verified by comparing with an experimental MSE, and it is demonstrated that the proposed precoding method is more beneficial than existing retransmission methods for the RN when the SN transfers both single and multiple data stream(s).

Machine Learning for CSI Recreation in the Digital Twin Based on Prior Knowledge

Knowledge of channel state information (CSI) is fundamental to many functionalities for mobile communication systems. With the advance of machine learning (ML) and digital maps, i.e., digital twins, we have a big opportunity to learn the propagation environment and design novel methods to derive and report CSI. In this work, we propose to combine untrained neural networks (UNNs) and conditional generative adversarial networks (cGANs) for MIMO channel recreation based on prior knowledge. The UNNs learn the prior-CSI for some locations which are used to build the input to a cGAN. Based on the prior-CSI estimates, their locations and the location of the desired channel, the cGAN is trained to output the channel expected at the desired location. This combined approach can be used for low overhead CSI reporting as, after training, we only need to report the desired location. Our results show that our CSI recreation method is successful in modelling the wireless channel under different configurations of prior-CSI spatial sampling. In addition, the results consider a real world measurement campaign for indoor line of sight and non-line of sight channels. The signal to noise ratio (SNR) achieved by our CSI recreation is better than the SNR reported by the measured campaign providers. Moreover, our CSI recreation provides means for low overhead CSI reporting as the UNN structure is underparameterized compared to the full explicit CSI, and only the desired location is needed for the cGAN to recreate the desired CSI.

A Multi-Cluster-Based Distributed CDD Scheme for Asynchronous Joint Transmissions in Local and Private Wireless Networks

In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks. The use of multiple clusters or small cells is adopted to reduce the transmission distance to users thereby increasing data-rates and reducing latency. To further increase the spectral efficiency and achieve flexible spatial degrees of freedom, we consider that a distributed remote radio unit system (dRRUS) is installed in each of the clusters. A key characteristic of deploying the dRRUS in private networks is the associated multipath-rich and asynchronous delay propagation environment. Therefore, we consider asynchronous multiple signal reception at the remote radio units and propose an intersymbol interference free distributed cyclic delay diversity (dCDD) scheme for JT to achieve the full transmit diversity gain without requiring full channel state information of the private network. The spectral efficiency of the proposed dCDD-based JT is analyzed by deriving a new closed-form expression, and then compared with link-level simulations for non-identically distributed frequency selective fading over the entire network. Due to its distributed structure, the dRRUS relies on backhaul communications between the private network server and cluster master (CM), which is the main backhaul connection, and between the CM to remote radio units, which are the secondary backhaul connections. Thus, it is important for us to investigate the impact of reliability of main and secondary backhaul connections on the system. Our results show that the resulting composite backhaul connections can be accurately modeled by our proposed product of independent Bernoulli processes.

Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems

Channel estimation in the RIS-aided massive multiuser multiple-input single-output (MU-MISO) wireless communication systems is challenging due to the passive feature of RIS and the large number of reflecting elements that incur high channel estimation overhead. To address this issue, we propose a novel cascaded channel estimation strategy with low pilot overhead by exploiting the sparsity and the correlation of multiuser cascaded channels in millimeter-wave massive MISO systems. Based on the fact that the phsical positions of the BS, the RIS and users may not change in several or even tens of consecutive channel coherence blocks, we first estimate the full channel state information (CSI) including all the angle and gain information in the first coherence block, and then only re-estimate the channel gains in the remaining coherence blocks with much less pilot overhead. In the first coherence block, we propose a two-phase channel estimation method, in which the cascaded channel of one typical user is estimated in Phase I based on the linear correlation among cascaded paths, while the cascaded channels of other users are estimated in Phase II by utilizing the partial CSI of the common base station (BS)-RIS channel obtained in Phase I. The total theoretical minimum pilot overhead in the first coherence block is $8J-2+(K-1)\left\lceil (8J-2)/L\right\rceil $, where $K$, $L$ and $J$ denote the numbers of users, paths in the BS-RIS channel and paths in the RIS-user channel, respectively. In each of the remaining coherence blocks, the minimum pilot overhead is $JK$. Moreover, the training phase shift matrices at the RIS are optimized to improve the estimation performance.

On the Sum-Rate of RIS-Assisted MIMO Multiple-Access Channels Over Spatially Correlated Rician Fading

Reconfigurable intelligent surface (RIS) stands out as a promising technology by enhancing the electromagnetic wave propagation environment with its passive reflecting elements. In this paper, we focus on the ergodic sum-rate analysis and maximization of the RIS-assisted uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) under Rician fading by exploiting full statistical channel state information (CSI). The spatial correlations at the base station, the users, and the RIS are also considered. By using the replica method originated in statistical physics, the closed-form asymptotic ergodic sum-rate of the system is first derived in the large-system regime on the account of the unique channel structure of the RIS-assisted MIMO-MAC system. Then, based on the derived asymptotic ergodic sum-rate, we propose an alternating optimization (AO) algorithm to jointly design the transmit covariance matrix of users and the phase-shifting matrix of RIS with full statistical CSI. Simulation results are also demonstrated to verify the accuracy of the derived asymptotic ergodic sum-rate and the superiority of the proposed AO algorithm. The results reveal that the derived closed-form asymptotic ergodic sum-rate matches very well with the Monte Carlo results even for a small number of antennas, and the proposed AO algorithm can achieve up to 10 bps/Hz gain at high signal-to-noise (SNR) regime, which is therefore valuable for future RIS-assisted MIMO-MAC system designs.

EVCsiNet: Eigenvector-Based CSI Feedback Under 3GPP Link-Level Channels

Recently, deep learning based methods have been widely used for wireless communications. In this letter, different from current researches considering full channel state information (F-CSI) feedback, the eigenvector based CSI feedback with deep learning approach is proposed, referred to as EVCsiNet, where the joint eigenvector concatenated from multiple subbands is compressed and recovered at the encoder and decoder, respectively. Simulation results verify the superiority of our schemes over CSI recovery accuracy and feedback overhead compared with conventional methods using codebooks on link-level channel models in different scenarios.

Scalable user selection in FDD massive MIMO

User subset selection requires full downlink channel state information to realize effective multi-user beamforming in frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. However, the channel estimation overhead scales with the number of users in FDD systems. In this paper, we propose a novel propagation domain-based user selection scheme, labeled as zero-measurement selection, for FDD massive MIMO systems with the aim of reducing the channel estimation overhead that scales with the number of users. The key idea is to infer downlink user channel norm and inter-user channel correlation from uplink channel in the propagation domain. In zero-measurement selection, the base-station performs downlink user selection before any downlink channel estimation. As a result, the downlink channel estimation overhead for both user selection and beamforming is independent of the total number of users. Then, we evaluate zero-measurement selection with both measured and simulated channels. The results show that zero-measurement selection achieves up to 92.5% weighted sum rate of genie-aided user selection on the average and scales well with both the number of base-station antennas and the number of users. We also employ simulated channels for further performance validation, and the numerical results yield similar observations as the experimental findings.

AI enlightens wireless communication: Analyses, solutions and opportunities on CSI feedback

In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group. Firstly, the framework of full channel state information (F-CSI) feedback problem and its corresponding channel dataset are provided. Then the enhancing schemes for DL-based F-CSI feedback including i) channel data analysis and preprocessing, ii) neural network design and iii) quantization enhancement are elaborated. The final competition results composed of different enhancing schemes are presented. Based on the valuable experience of 1st WAIC, we also list some challenges and potential study areas for the design of AI-based wireless communication systems.

Feedback-Free Adaptive Modulation Selection Algorithm for FSO Systems

Free space optical (FSO) communication systems experience turbulence-induced fading. As a possible solution to mitigate the effects of fading, adaptive transmission that allows the adjustment of transmission parameters such as transmit power and modulation size can be used. Most of the existing works on adaptive FSO systems assume the presence of full channel state information (CSI) at the transmitter side. In practice, incomplete/outdated CSI may prevent the complete extraction of the theoretical gain expected from adaptive systems. In this letter, to avoid the difficulties associated with feedback, we propose a feedback-free adaptive modulation selection algorithm for FSO systems. We formulate the design as an optimization problem where average throughput is maximized by selecting the largest modulation order under the constraint that the required transmit power for a targeted bit error rate (BER) value does not exceed the maximum transmit power. We further present numerical results to confirm the superiority of the proposed adaptive algorithm.

Secure Cognitive Radio Communication via Intelligent Reflecting Surface

In this paper, an intelligent reflecting surface (IRS) assisted spectrum sharing underlay cognitive radio (CR) wiretap channel (WTC) is studied, and we aim at enhancing the secrecy rate of secondary user in this channel subject to total power constraint at secondary transmitter (ST), interference power constraint (IPC) at primary receiver (PR) as well as unit modulus constraint at IRS. Due to extra IPC and eavesdropper (Eve) are considered, all the existing solutions for enhancing secrecy rate of IRS-assisted non-CR WTC as well as enhancing transmission rate in IRS-assisted CR channel without eavesdropper fail in this work. Therefore, we propose new numerical solutions to optimize the secrecy rate of this channel under full primary, secondary users’ channel state information (CSI) and three different cases of Eve’s CSI: full CSI, imperfect CSI with bounded estimation error, and no CSI. To solve the difficult non-convex optimization problem, an efficient alternating optimization (AO) algorithm is proposed to jointly optimize the beamformer at ST and phase shift coefficients at IRS. In particular, when optimizing the phase shift coefficients during each iteration of AO, a Dinkelbach based solution in combination with successive approximation and penalty based solution is proposed under full CSI and a penalty convex-concave procedure solution is proposed under imperfect Eve’s CSI. For no Eve’s CSI case, artificial noise (AN) aided approach is adopted to help enhancing the secrecy rate. Simulation results show that our proposed solutions for the IRS-assisted design greatly enhance the secrecy performance compared with the existing numerical solutions with and without IRS under full and imperfect Eve’s CSI. And positive secrecy rate can be achieved by our proposed AN aided approach given most channel realizations under no Eve’s CSI case so that secure communication also can be guaranteed. All of the proposed AO algorithms are guaranteed to monotonic convergence.

Optical Index-Coded Space Shift Keying (IC/SSK)

In this letter, a new channel coding technique is proposed for optical spatial shift keying (SSK)-based visible light communications (VLC) systems by introducing redundancy in the spatial domain. The information is encoded by the redundant indices of the light emitting diodes (LEDs), assuming full channel state information (CSI) at the transmitter. The efficient construction of index-coding (IC) is presented with low computational complexity. Depending on the number of LEDs and the PDs as well as the amount of redundancy, it is concluded that IC/SSK achieves excellent coding gains at full rate and given target bit error rate (BER), as contrasted to the conventional channel coding in bit-domain, and yields superior BER performance in the presence of the VLC <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blockage channels</i> as compared to the uncoded SSK systems.