Full Channel State Information Research Articles

When Spectrum Sharing in Cognitive Networks Meets Deep Reinforcement Learning: Architecture, Fundamentals and Challenges

Next-generation wireless networks require the integration of cognitive networks (CNs) and decision-making techniques to improve the spectrum efficiency. The conventional spectrum sharing schemes require full channel state information of CNs and cannot satisfy the low-latency requirement of nextgeneration wireless networks. Artificial intelligence has shown its high potential to perform decision-making and improve resource utilization efficiency. Hence, we propose a multi-agent reinforcement learning (MARL)-based scheme for spectrum sharing, which is a promising self-decision technique in highly dynamic and complex wireless networks. We analyze several key challenges when MARL is applied to spectrum sharing, such as multi-objective function formulation, multi-dimensional action space, and partial channel state information. Then, we propose efficient solutions and apply the explainable DRL to improve the convergence efficiency in spectrum sharing. The proposed architecture, fundamentals, and challenges provide a clear vision for MARL in CNs.

Application of Non-orthogonal Multiple Access using Reconfigurable Antenna in 5G Communication

This paper aims to realize a new multiple access technique based on recently proposed 5G communication. Integration of the presented reconfigurable antenna systems with the well-known non-orthogonal multiple access (NOMA) technique causes a significant degradation in sum rate due to the predictable power division in reconfigurable antennas. To circumvent this fundamental limit, a new multiple access technique is proposed. The integration of reconfigurable antenna systems with NOMA is a logical step in leveraging the capabilities of both technologies. The integration of reconfigurable antenna systems with NOMA is a logical step in leveraging the capabilities of both technologies. The technique which is called reconfigurable antenna non orthogonal multiple access (RA-NOMA) is proposed here. Three different cases are considered, i.e., OMA, RAMA-OMA and RA-NOMA with partial and full channel state information (CSI). In the first case, orthogonal multiple access is employed only. In second case RAMA is applied for OMA to enhance the performance of OMA. In the third case, RAMA with full CSI is applied to NOMA for both symmetric and asymmetric channels. All three cases are simulated in MATLAB environments. The simulation plots demonstrate the analytical findings.

ALAMOUTI SPACE-TIME CODING FOR VEHICULAR COMMUNICATIONS IN THE PRESENCE OF CHANNEL ESTIMATION ERRORS

In this paper, the error performance of the Alamouti space-time coding (STC) scheme is investigated for vehicle-to-vehicle (V2V) communication systems over imperfect cascaded fading channels. In vehicular communication systems, perfect knowledge of the channel state information is not available to the users at all times due to the rapid movement of communicating vehicles and fast change of the rich scattering environment which makes the fading effects in wireless channels more severe. Therefore, in the analysis, we consider the erroneous estimation of the channel gain, which is more realistic for practical scenarios. For this purpose, we first derive the moment-generation function (MGF) of the channel fading coefficient with estimation error in the case of the cascaded Nakagami-m fading conditions. Then, using the MGF, we obtain the closed-form symbol-error-rate (SER) expressions of Alamouti STC with two transmitting and L receiving antennas for the M-PSK and M-QAM modulation schemes. Then, the exact ergodic capacity expression is derived for the proposed system. Furthermore, the analytical results are verified through Monte-Carlo simulations. Numerical results show that the SER performance of V2V communication systems can be improved significantly by using Alamouti STC even in case of harsh fading conditions and full channel-state information is not available due to estimation errors.

Low-cost relay selection in multihop cooperative networks

A best relay selection algorithm for a cooperative multi-hop cross-layer single-input single-output (SISO) amplify-and-forward (AF) wireless relay network is analyzed in this work, with the application where exact channel state information (CSI) is known. We present algorithmic strategies to simplify a multi-hop parallel SISO relay network into a series multi-hop network by finding the best path having the maximum received signal-to-noise ratio (SNR). The best relay selection by using dynamic programming search entails high computations and large memory requirements, as well as involves the full CSI information, making this approach impractical for large-scale networks. The goal of the proposed low-cost near-optimal routing strategy in this work is to provide close to optimal performance with much less complexity compared to traditional routing. Next, we also propose a low-cost power allocation to further improve system performance over traditional power allocation found in the literature. Computer simulations show excellent performance improvement of our proposed methods in terms of bit error rate (BER) as well as outage probability over the traditional relaying and power allocation algorithms.

Channel Customization for Limited Feedback in RIS-Assisted FDD Systems

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems. In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed. Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

When To Use CSI (Combined Senory Index) in Electro-diagnostic Testing for Carpal Tunnel Syndrome (CTS) (P13-8.014)

<h3>Objective:</h3> The lack of a gold standard is the fundamental problem in diagnosing CTS. The objective of this study was to evaluate the utility and appropriate use of CSI in diagnosing mild CTS. <h3>Background:</h3> CTS accounts for 90% of neuropathies, with a prevalence of ~270/100,000. Diagnosis is based on clinical complaints, neurologic examination, provocative maneuvers, and electrodiagnostic studies. Treatment is conservative or surgical depending on severity of symptoms. CTS is the most common reason for EMGs in most labs and the most common EMG finding. NCS/EMG is helpful in diagnosing CTS, providing objective information regarding the median nerve’s physiologic health and degree of damage. The efficacy of CSI has been debated as a confirmatory test. <h3>Design/Methods:</h3> This is a retrospective, multi-center case series of 69 limb studies in 57 patients who were evaluated between July 2021 and June 2022 who underwent NCS/EMG and CSI testing to evaluate for mild CTS. Median nerve peak/distal latencies, conduction velocities, SNAPs, CSI parameters (median-radial latency comparison of digit I, median-ulnar latency comparison of digit IV, and trans-palmar median-ulnar latency comparison), and provocative maneuvers (Tinel’s and Phalen’s) were included. Patients who have abnormal NCS or who did not complete full CSI were excluded. <h3>Results:</h3> There was no correlation between normal CTS studies and a positive CSI. Tinel’s testing was nonspecific and nonsensitive with low PPV/NPV. Phalen’s testing had a relatively high specificity, but low sensitivity and low PPV/NPV. There was a relatively high correlation of positive CSI studies when each portion was performed in this population. 27/69 CSI’s were abnormal in this study. The overall yield was ~39% <h3>Conclusions:</h3> Our study showed a decent yield, identifying 39% of cases that would have otherwise been missed. While only speculative, this lab sees benefit in CSI, as positive findings may reduce further testing, quiet patient concerns, and direct surgical vs conservative management. <b>Disclosure:</b> Dr. Imbs has nothing to disclose. Dr. Ramzan has nothing to disclose. Mr. French has nothing to disclose. Dr. Ambe has nothing to disclose. Dr. Nguyen has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for Argenx. Dr. Nguyen has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for ArgenX. Dr. Nguyen has received publishing royalties from a publication relating to health care.

Physics implications of a combined analysis of COHERENT CsI and LAr data

The observation of coherent elastic neutrino nucleus scattering has opened the window to many physics opportunities. This process has been measured by the COHERENT Collaboration using two different targets, first CsI and then argon. Recently, the COHERENT Collaboration has updated the CsI data analysis with a higher statistics and an improved understanding of systematics. Here we perform a detailed statistical analysis of the full CsI data and combine it with the previous argon result. We discuss a vast array of implications, from tests of the Standard Model to new physics probes. In our analyses we take into account experimental uncertainties associated to the efficiency as well as the timing distribution of neutrino fluxes, making our results rather robust. In particular, we update previous measurements of the weak mixing angle and the neutron root mean square charge radius for CsI and argon. We also update the constraints on new physics scenarios including neutrino nonstandard interactions and the most general case of neutrino generalized interactions, as well as the possibility of light mediators. Finally, constraints on neutrino electromagnetic properties are also examined, including the conversion to sterile neutrino states. In many cases, the inclusion of the recent CsI data leads to a dramatic improvement of bounds.

Net-Zero Energy Dual-Functional Radar-Communication Systems

This paper proposes beamforming designs for net-zero energy multi-input multi-output (MIMO) dual-functional radar-communication (DFRC) systems that are powered through energy harvesting (EH) resources and aim to operate autonomously without access to the power grid. We propose a weighted optimization problem to jointly maximize the radar mutual information and minimum quality of service (QoS) requested by communication users subject to energy balancing constraints. The proposed problem is not convex, hence it is tough to solve. We exploit semidefinite relaxation (SDR) and first-order Taylor expansion techniques to relax its non-convexity issues. We then propose an iterative algorithm to obtain the beamforming matrices for the reference scenario when full channel state information (CSI) and energy arrival information (EAI) are available. For the single-target scenario, we show that the proposed optimization contains rank-one solutions. For the multiple targets scenario, by adding auxiliary optimization variables, we show that rank-one matrices can be achieved from the optimal solutions of the proposed optimization. We then propose a robust optimization for the case where only imperfect CSI and EAI are assumed to be known. Finally, numerical simulations show that the proposed DFRC designs are convergent and obtain a graceful trade-off between the radar and communication performances.

Learning-Based MIMO Channel Estimation Under Practical Pilot Sparsity and Feedback Compression

Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks. Precoding in Massive MIMO transmission requires accurate downlink channel state information (CSI). Many recent works have effectively applied deep learning (DL) to jointly train UE-side compression networks for delay domain CSI and a BS-side decoding scheme. Vitally, these works assume that the full delay domain CSI is available at the UE, but in reality, the UE must estimate the delay domain based on a limited number of frequency domain pilots. In this work, we propose a linear pilot-to-delay estimator (P2DE) that acquires the truncated delay CSI via sparse frequency pilots. We show the accuracy of the P2DE under frequency downsampling, and we demonstrate the P2DE’s efficacy when utilized with existing CSI estimation networks. Additionally, we propose to use trainable compressed sensing (CS) networks in a differential encoding network for time-varying CSI estimation, and we propose a new network, MarkovNet-ISTA-ENet (MN-IE), which combines a CS network for initial CSI estimation and multiple autoencoders to estimate the error terms. We demonstrate that MN-IE has better asymptotic performance than networks comprised of only one type of network.

Delay-Oriented Scheduling in 5G Downlink Wireless Networks Based on Reinforcement Learning With Partial Observations

5G wireless networks are expected to satisfy different delay requirements of various traffics by network resource scheduling. Existing scheduling methods perform poorly in practice due to their unrealistic assumption on the access to the full channel state information (CSI) or the explicit mathematical expression of network delay. In this paper, we consider the delay-oriented packet scheduling problem in multi-cell 5G downlink networks with multiple users and traffic types (e.g., FTP, VoIP and video streaming), and formulate it as a partially observable Markov decision process (POMDP). We design a delay-oriented downlink scheduling framework based on deep reinforcement learning (DRL) to autonomously schedule the active traffic flows without the full channel information. Furthermore, a recurrent proximal policy optimization (RPPO) algorithm is proposed to perceive the underlying state and accelerate learning under different time granularities, with the policy gradient theorem under POMDP strictly proved. By incorporating the future traffic information provided by a proposed spatial-temporal prediction algorithm, RPPO can balance the load and achieve lower delay in real-time multi-cell multi-user scenarios. Results of extensive experiments on a realistic 5G simulator demonstrate that our framework significantly outperforms existing approaches in terms of both tail delay and average delay for up to 48% and 41.7%, respectively.

Secrecy Outage Analysis of Relay Aided Multiuser Scheduling for Wireless Networks in the Face of Co-Channel Interference

In this paper, we analyze the secrecy outage probability of a relay aided multiuser network which consists of a cluster head (CH), a decode-and-forward (DF) relay, and multiple users in the presence of an eavesdropper and co-channel interference (CCI). Firstly, we propose two relay aided multiuser scheduling (RMS) schemes to improve the secrecy outage performance of the network by considering the availability of channel state information (CSI) of CCI links, including the partial CSI based RMS (PCSI-RMS) scheme and the full CSI based RMS (FCSI-RMS) scheme. For comparison purposes, the relay aided round-robin scheduling (RRRS) scheme is also employed as a baseline. Then, we derive closed-form expressions for the proposed PCSI-RMS and FCSI-RMS schemes as well as the RRRS scheme in terms of their exact and asymptotic secrecy outage probabilities over Rayleigh fading channels in the face of CCI. Finally, numerical results show that the secrecy outage performance of the proposed PCSI-RMS and FCSI-RMS schemes is better than that of the RRRS scheme, where FCSI-RMS achieves the best secrecy outage performance. With an increase of the number of users, the secrecy outage performance of the PCSI-RMS and FCSI-RMS schemes is significantly improved, while that of the RRRS scheme keeps unchanged, which demonstrates the benefits of adopting the proposed PCSI-RMS and FCSI-RMS schemes against eavesdropping in CCI environments. Moreover, there is an optimal power allocation factor between the CH and the relay, which makes the network have the best secrecy outage performance.

On the Secrecy Design of STAR-RIS Assisted Uplink NOMA Networks

This paper investigates the secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access system, where the legitimate users send confidential signals to the base station by exploiting STAR-RIS to reconfigure the electromagnetic propagation environment proactively. Depending on the availability of the eavesdropping channel state information (CSI), both the full CSI and statistical CSI of the eavesdropper are considered. For the full eavesdropping CSI scenario, we adopt the adaptive-rate wiretap code scheme with the aim of maximizing minimum secrecy capacity subject to the successive interference cancellation decoding order constraints. To proceed, we propose an alternating hybrid beamforming (AHB) algorithm to jointly optimize the receive beamforming, transmit power, and reflection/transmission coefficients. While for the statistical eavesdropping CSI scenario, the constant-rate wiretap code scheme is employed to minimize the maximum secrecy outage probability (SOP) subject to the quality-of-service requirements of legitimate users. Then, we derive the exact SOP expression under the constant-rate coding strategy and develop an extended AHB algorithm for the joint secrecy beamforming design. Simulation results demonstrate the effectiveness of the proposed scheme. Moreover, some useful guidance about the quantification of phase shift/amplitude and the deployment of STAR-RIS is provided.

Resource Allocation Policies for Battery Constrained Energy Harvesting Communication Systems With Co-Channel Interference

This paper studies resource allocation policies for energy harvesting (EH) multi-user multiple input single output (MU- MISO) communication systems. The multi-antenna EH base station (BS) is equipped with a limited-capacity battery. Though employing the multi-antenna at the BS provides the channel diversity, this leads to the co-channel interference which makes the resource allocation problem hard to solve. For this challenging scenario, we first consider off-line policies based on full channel state information (CSI) and energy arrival information (EAI) to obtain the best performance for any feasible resource allocation policies. We propose an iterative algorithm using generalized linear fractional programming to obtain an optimal policy. To achieve a low-complexity sub-optimal policy, we propose another iterative algorithm using the successive convex approximation. Based on the off-line policies, we develop on-line policies in which only statistical CSI and EAI are available. The complexity of the proposed policies is derived. Finally, simulation results evaluate the performance of the proposed approaches and show that the proposed polices outperform the benchmark.

Wireless Information Surveillance Over Multiple Time Slots

Proactive eavesdropping via joint jamming and relaying has been proposed to improve the legitimate surveillance performance. However, how will it perform over multiple time slots compared to proactive eavesdropping via jamming remains unknown. To answer this question, proactive eavesdropping via joint jamming and relaying over multiple time slots is investigated with full and causal channel state information (CSI). The problem for maximizing the average eavesdropping rate subject to the peak and average transmit power constraints is investigated, and the online and offline algorithms are proposed. Simulation results confirm that the proposed algorithms are low complex and practically optimal. It is shown that compared to proactive eavesdropping via jamming, the joint jamming and relaying always performs better with full CSI, and performs better with causal CSI when the average transmit power constraint is loose.

Joint User Association and Hybrid Beamforming Designs for Cell-Free mmWave MIMO Communications

Cell-free millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communications have been proposed as promising enablers for the next generation wireless networks. In this paper, we study the user association and hybrid beamforming in cell-free mmWave systems without full channel state information (CSI) acquisition. We consider a cloud radio access network (C-RAN), where multiple remote radio heads (RRHs) are distributed to communicate with users via analog beamforming, and connected to a centralized baseband unit (BBU) through fronthaul links which executes digital beamforming. We aim to jointly design user association, hybrid beamforming, and fronthaul compression with the aid of uplink training. A train-and-design framework is developed to achieve this goal. In particular, we first propose a two-stage uplink training approach to assist RRH-level design, during which the analog beamforming and user association are obtained. After that, digital beamforming and fronthaul compression are optimized at BBU based on the training results. Two performance metrics are considered in this paper, i.e. weighted sum-rate maximization and max-min fairness. Simulation results demonstrate the effectiveness of the proposed train-and-design framework for both sum-rate maximization and max-min fairness performance metrics. It is shown that the proposed algorithms can achieve comparable performance to the full-digital beamformer.

NOMA Joint Channel Estimation and Signal Detection Using Rotational Invariant Codes and GMM-Based Clustering

This letter studies the joint channel estimation and signal detection for the uplink power-domain non-orthogonal multiple access. The proposed technique performs both detection and estimation without the need of pilot symbols by using a clustering technique. We apply rotational-invariant coding to assist signal detection at the receiver without sending pilot symbols. We utilize Gaussian mixture model (GMM) to automatically cluster the received signals without supervision and optimize decision boundaries to improve the bit error rate (BER) performance. Simulation results show that the proposed scheme without using any pilot symbol achieves almost the same BER performance as that for the conventional maximum likelihood receiver with full channel state information.

Ефективність системи трисотової мережі для граничних користувачів з використанням попереднього кодування, регулювання завади і зворотним зв’язком квантованого обмеженого каналу

Користувачі на загальній границі багатокористувацької трисотової MIMO системи з низхідною лінією зв’язку страждають від низької потужності сигналу у власній соті, а також від сигналу завад рівної потужності від сусідніх двох сот. В даному випадку для покращення загальної роботи системи застосовуються схеми придушення завад. Крім того, повний CSI зворотний зв’язок від користувачів до базової станції сильно навантажує канал зворотного зв’язку. Зворотний зв’язок інформаційного квантованого редукованого каналу може зменшити навантаження майже наполовину на зворотний канал від користувача до передавача, та підвищити швидкість передачі даних системи. Методи попереднього кодування на основі кодової книги LTE покращують сумарну швидкість. Розроблено метод попереднього кодування з використанням сингулярного розкладання SVD (singular value decomposition) на вибраних кодових векторах на основі максимізації вектору еквівалентного коду каналу SVD-ECC (SVD Equivalent Channel-Code) і максимізації вектору SINR-коду SVD-SINRC (SVD SINR-Code). Обидва методи забезпечують вищу сумарну швидкість передачі даних системи в порівнянні з методом попереднього кодування на основі ZF (zero forcing), причому швидкість більша для SVD-ECC, ніж для SVD-SINRC. Також проведено дослідження для користувачів з ортогональними еквівалентними каналами та подальшим SVD на вибраному каналі користувача SVD-ORT (SVD orthogonal). Отримано результати зі зміною SNR користувача, кількості вибраних користувачів K і загальної кількості активних користувачів L у соті.

Uplink Performance of Cell-Free Massive MIMO With Multi-Antenna Users Over Jointly-Correlated Rayleigh Fading Channels

In this paper, we investigate a cell-free massive MIMO system with both access points (APs) and user equipments (UEs) equipped with multiple antennas over jointly-correlated Rayleigh fading channels. We study four uplink implementations, from fully centralized processing to fully distributed processing, and derive their achievable spectral efficiency (SE) expressions with minimum mean-squared error successive interference cancellation (MMSE-SIC) detectors and arbitrary combining schemes. Furthermore, the global and local MMSE combining schemes are derived based on full and local channel state information (CSI) obtained under pilot contamination, which can maximize the achievable SE for the fully centralized and distributed implementation, respectively. We study a two-layer decoding implementation with an arbitrary combining scheme in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. Besides, we compute novel closed-form SE expressions for the two-layer decoding implementation with maximum ratio (MR) combining. In the numerical results, we compare the SE performance for different implementation levels, combining schemes, and channel models. It is important to note that increasing the number of antennas per UE may degrade the SE performance.

Learning the CSI Recovery in FDD Systems

We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, the base station reconstructs the full channel state information in the downlink frequency range based on limited downlink channel state information feedback from the mobile terminal. The channel state information recovery is based on a convolutional neural network which is trained exclusively on collected channel state samples acquired in the uplink frequency domain. No acquisition of training samples in the downlink frequency range is required at all. Finally, after a detailed presentation and analysis of the proposed technique and its performance, the “reciprocity of distribution” assumption of the convolutional neural network that is central to the proposed approach is validated with an analysis based on the maximum mean discrepancy metric.

Deep Learning-Based Resource Allocation for Device-to-Device Communication

In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized for maximization of the overall spectral efficiency whilst maintaining the quality-of-service (QoS) of the cellular users. Depending on the availability of channel state information (CSI), two different configurations are considered, namely 1) centralized operation with full CSI and 2) distributed operation with partial CSI, where in the latter case, the CSI is encoded according to the capacity of the feedback channel. Instead of solving the resulting resource allocation problem for each channel realization, a DL framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Furthermore, we propose a new training strategy that combines supervised and unsupervised learning methods and a local CSI sharing strategy to achieve near-optimal performance while enforcing the QoS constraints of the cellular users and efficiently handling the integer optimization variables based on a few ground-truth labels. Our simulation results confirm that near-optimal performance can be attained with low computation time, which underlines the real-time capability of the proposed scheme. Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. Furthermore, we show that the proposed DL framework can be easily extended to communication systems with different design objectives.