Partial Channel State Information Research Articles

Learning-Based Resource Allocation for Ultra-Reliable V2X Networks With Partial CSI

In this paper, we study the resource allocation in high mobility vehicle-to-everything (V2X) networks with only slowly varying large-scale channel parameters. For satisfying the diversity requirements of different types of links, i.e., low delay for vehicle-to-infrastructure (V2I) connections and ultra-reliability for vehicle-to-vehicle (V2V) connections, we formulate a joint power, spectrum and vehicle local computing ratio allocation problem to minimize the delay of V2I links whilst satisfying the V2V reliability constraint. For solving the formulated problem, a Feasible Region Transformation Method is firstly developed to convert the probabilistic V2V reliability requirement into a computable constraint. In addition, a Robust Signal to Interference Plus Noise Ratio (SINR) Modified Method is proposed to give the computable expression for the V2I throughput. Then, a parallel Deep Neural Network (DNN) framework is designed for the resource allocation in V2X networks, where one is the transmit power control unit and the other is the local computing ratio allocation unit. After that, a Feedback-oriented Learning Method is proposed to train the parallel DNN-based resource allocation framework, in which the output of DNN is used as feedback to dynamically revise the training loss function along with the training process. Afterwards, the Hungarian method is employed to obtain the optimal spectrum matching. Finally, we conduct the simulations to show that the proposed learning-based algorithm has better performance compared with other general algorithms.

Goodput Maximization With Quantized Feedback in the Finite Blocklength Regime for Quasi-Static Channels

In this paper, we study a quantized feedback scheme to maximize the goodput of a finite blocklength communication scenario over a quasi-static fading channel. It is assumed that the receiver has perfect channel state information (CSI) and sends back the CSI to the transmitter over a resolution-limited error-free feedback channel. With this partial CSI, the transmitter is supposed to select the optimum transmission rate, such that it maximizes the overall goodput of the communication system. This problem has been studied for the asymptotic blocklength regime, however, no solution has so far been presented for finite blocklength. Here, we study this problem in two cases: with and without constraint on reliability. We first formulate the optimization problems and analytically solve them. Iterative algorithms that successfully exploit the system parameters for both cases are presented. It is shown that although the achievable maximum goodput decreases with shorter blocklengths and higher reliability requirements, significant improvement can be achieved even with coarsely quantized feedback schemes.

Adaptive Power Control for Cooperative Covert Communication With Partial Channel State Information

In jammer-assisted covert communication scenarios, jamming signals also degrade the communication between legitimate users. Considering that it is difficult for a transmitter to obtain all the required channel state information (CSI), we propose an adaptive power control strategy with partial CSI, which decreases the outage probability while bringing more uncertainty to a warden. Specifically, we adopt an upper confidence limit to estimate the unknown CSI from the jammer to the receiver. Under warden’s optimal detector, we analyze the maximum covert throughput by optimizing the upper confidence limit. A closed-form expression of the optimized upper confidence limit is derived in certain cases. The numerical results validate the effectiveness of the proposed strategy and guide the estimation of unknown CSI.

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.

Anchor-Assisted Channel Estimation for Intelligent Reflecting Surface Aided Multiuser Communication

Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the channels among the fixed BS, IRS, and two anchors are estimated in a large timescale, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.

A Cooperative D2D Content Sharing Scheme Using NOMA Under Social Ties

With the merit of bypassing the base station (BS) to directly communicate between user equipment (UE), D2D content sharing between socially connected users has revealed promising potential in future networks. However, D2D receivers (DRs) in different D2D pairs (DPs) may also have demand for additional content sharing services from the D2D transmitters (DTs), who also have considerable social ties with them, in other surrounding DPs to enhance or supplement their received sharing services. In this letter, we propose a cooperative D2D content sharing scheme using NOMA technique under social ties, where the DRs in different DPs, who have social similarities in interested files, separately spare a portion of the transmit power of their corresponding DT to each other to improve the received sharing services in DRs. Based on this, the performance of the proposed scheme underlaying cellular network with partial channel state information (CSI) is analyzed, which is suitable for distributed deployment. Finally, the simulations validate the effectiveness of our proposed scheme.

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.

Interplay Between Interference-Aware Resource Allocation Algorithm Design, CSI, and Feedback in Underlay D2D Networks

A key problem in underlay device-to-device (D2D) systems is assigning cellular users and D2D users to subchannels to improve spatial reuse while controlling the interference they cause to each other. We present a unified treatment of this problem for two practically motivated partial and statistical channel state information (CSI) models with quantized feedback. They differ in the CSI available at the D2D receiver. In both models, the nodes only have statistical information of inter-D2D and inter-cell interferences, and employ fractional power control. We present two polynomial-time algorithms to assign multiple D2D pairs to subchannels, namely, relaxation-pruning algorithm (RPA) and cardinality-constrained subchannel assignment algorithm (CCSAA). RPA and CCSAA guarantee a D2D sum rate that is at least one-half and one-third, respectively, of the optimal sum rate. We also propose a novel statistical rate upgradation technique that exploits the allocation information to improve the D2D rates. We observe that inter-D2D interference has a more pronounced effect in the statistical CSI model. The algorithms respond differently to the two CSI models. RPA outperforms CCSAA in the partial CSI model, while CCSAA outperforms RPA in the statistical CSI model despite its weaker performance guarantee.

Resource allocation for cellular device‐to‐device‐aided vehicle‐to‐everything networks with partial channel state information

AbstractIn Vehicle‐to‐Everything (V2X), cellular Device‐to‐Device (D2D) communication can not only enhance system capacity and spectral utilization but also reduce the traffic load and communication delay. However, due to the fast variations of the channel state information (CSI) caused by high mobility of vehicles, the accurate CSI is difficult to be obtained in real‐time. In this paper, different from other previous researches that are subject to the perfect CSI, a more realistic channel state model is adopted that only the partial CSI of the communication links is available. Then we study the joint power allocation and spectrum sharing problem to maximize the total weighted ergodic capacity of all vehicular links, namely, Vehicle‐to‐Infrastructure (V2I) links and Vehicle‐to‐Vehicle (V2V) links. Meanwhile, the transmission reliability guarantees the quality of service (QoS) requirements of V2I and V2V. Since the formulated problem is a nonconvex mixed integer nonlinear programming (MINLP) problem, we first derive the strategy for obtaining the optimal solution of power allocation under arbitrary weighted factor and quantify different cases of feasible regions. Then we propose a two‐stage algorithm to complete the optimal resource allocation in polynomial time. Moreover, we further propose a low‐complexity spectrum sharing method based on the principles of Gale‐Shapley mechanism. The simulation results show that the proposed algorithms outperform the other baseline schemes significantly, and the low‐complexity scheme can effectively achieve a tradeoff between performance and complexity.

Partial and statistical CSI based precoding for reduced‐CP OTFS with inter‐slot‐interference pre‐cancellation

This work studies the precoding methods for the reduced cycle-prefix (CP) orthogonal time frequency space (OTFS) modulation to simplify the receiver design. First, a system model is established which shows that there are both intra-slot-interference and inter-slot-interference in the reduced-CP OTFS system. Then, the precoding operation is proposed to pre-cancel the inter-slot-interference in the reduced-CP OTFS system so that the bit error rate (BER) performance can be greatly improved. According to how much channel state information (CSI) needs to be fed back, precoding methods for two different cases are proposed: the partial CSI feedback case and the partial and statistical CSI feedback case. Among them, the partial and statistical CSI based precoding method has very small feedback burden. Simulation results of the BER performance of the proposed precoding methods for reduced-CP OTFS are reported and compared under different system parameters.

Performance analysis of spatial multiplexing MIMO-MFSK based on energy detection for fast-fading environments

In fast-fading scattering environments such as high-speed rail and low-altitude communications, mobile communication systems need to quickly and robustly estimate and equalize fast-fading, time-varying channels. Under these circumstances, noncoherent multiple-input multiple-output (MIMO) has received attention in recent years, since it is less influenced by factors such as phase fluctuations and has fewer requirements for channel estimation and synchronization. Spatial multiplexing MIMO-MFSK based on energy detection is different from conventional noncoherent MIMO in that it can achieve higher spatial multiplexing gain in the independent distribution of channel fading statistics. At the receiver, partial real channel state information (CSI) is available, which can improve the capacity while ensuring the reliability of the link. With partial real CSI replacing instantaneous CSI, the system performance is inferior to conventional coherent MIMO under the influence of noise. As a result, it is necessary to analyze its theoretical detection performance. By energy detection, the noise of the MIMO-MFSK system conforms to the generalized gamma distribution. On the basis of this distribution, the optimal decision rule of the system and the symbol error rate (SER) formula are derived. Additionally, we investigate the signal-dependent noise problem of minimum Euclidean distance detection. Numerical results show that the SER formula fits well with the simulation results under the condition of a high signal-to-noise ratio.

Relay selection schemes for SWIPT-enabled cooperative wireless networks with partial CSI

This study investigates the problem of relay selection in a cooperative network with Nakagami-m fading channels, where the energy of the relays is solely replenished by simultaneous wireless information and power transfer (SWIPT). Two efficient relay selection algorithms which require partial channel state information (CSI) at the source are proposed. The first scheme selects a relay based on the fading channel qualities of the source-relay and the relay-destination. The second one selects a relay based on the harvested power at relay and the fading channel quality of the relay-destination. In terms of coverage probability, the latter scheme is superior and is shown to have a performance close to that of the optimal relay selection scheme. Furthermore, the analytical performance bounds for the two proposed schemes are formulated. The performance analysis along with the simulation results are presented, demonstrating the superiority of the proposed schemes over the existing algorithms.

Deep Reinforcement Learning-Based Resource Allocation for Cellular Vehicular Network Mode 3 with Underlay Approach.

Vehicle-to-vehicle (V2V) communication has attracted increasing attention since it can improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links need to reuse the spectrum resources preoccupied with vehicle-to-infrastructure (V2I) links, which will interfere with the V2I links. Therefore, how to allocate wireless resources flexibly and improve the throughput of the V2I links while meeting the low latency requirements of the V2V links needs to be determined. This paper proposes a V2V resource allocation framework based on deep reinforcement learning. The base station (BS) uses a double deep Q network to allocate resources intelligently. In particular, to reduce the signaling overhead for the BS to acquire channel state information (CSI) in mode 3, the BS optimizes the resource allocation strategy based on partial CSI in the framework of this article. The simulation results indicate that the proposed scheme can meet the low latency requirements of V2V links while increasing the capacity of the V2I links compared with the other methods. In addition, the proposed partial CSI design has comparable performance to complete CSI.

Robust Design of Multicell D2D Communication Under Partial CSI

We consider device-to-device (D2D) communication underlaid in a cellular network to share the uplink resource of cellular users (CUs). It is a key the emerging Internet of Things to support vehicle-to-everything communication networks. In a multicell scenario, both D2D pairs and CUs may cause significant significant intercell interference (ICI) to the neighboring cells. Furthermore, due to substantial signaling overhead, we assume only partial channel state information (CSI) of D2D links at the base station. We consider joint power control, beamforming, and CU-D2D matching problem, assuming partial CSI from D2D pairs under the general Nakagami fading model. We formulate a joint receive beamforming and robust power control optimization problem for a CU-D2D pair to expected sum rate under the power budget, while meeting the minimum SINR requirements and worst case ICI limits at neighboring cells in a probabilistic sense. We propose an efficient algorithm that combines an iterative D2D feasibility check and a ratio-of-expectation approximation. A performance upper bound is also developed for benchmarking. For multiple CUs and D2D pairs, due to orthogonal channelization within each cell, we first focus on the problem of joint power control and beamforming for a CU-D2D pair and show how our proposed solution can be leveraged to find a solution for this general problem. The complexity analysis of the proposed approach is also provided. Simulation results show that the proposed algorithm gives performance close to the upper bound.

Learning Indoor Environment for Effective LiFi Communications: Signal Detection and Resource Allocation

Seamless light fidelity (LiFi) communications in a realistic indoor environment faces a number of important challenges including the loss of line-of-sight (LOS) link due to the random orientation of mobile devices or blockage by users or furniture in the room. These effects make the frequency response of the LiFi channel highly environment-dependent. The dynamic nature of the channel is not only dependent on the geometrical configuration of the room but also on the user behavior. In this paper, we investigate the hypothesis that, deep learning (DL) based LiFi communication techniques can effectively learn the distinct features of the indoor environment and user behavior to provide superior performance compared to conventional channel estimation techniques particularly when access to real-time channel state information (CSI) is restricted. In particular, we implement DL methods in two different problems, namely, signal detection and resource allocation for orthogonal frequency division multiplexing (OFDM) LiFi systems. To effectively test this hypothesis, a realistic LiFi channel impaired by random device orientation and blockage is simulated considering different geometrical configurations and user behavior in an indoor environment, e.g. presence or lack of furniture and varying user distribution defined by a conditional hotspot model. The simulation results confirm that DL-based LiFi systems with partial CSI are able to offer close performance to the optimal signal detection and resource allocation with perfect CSI. Moreover, the DL-based techniques demonstrate substantial gain against conventional benchmark schemes that employ channel estimation algorithms such as least squares (LS) or minimum mean square error (MMSE) and this gain increases in a more realistic or complex indoor environment, e.g. room with furniture or a hotspot scenario.

User Selection for Massive MIMO under Line-of-Sight Propagation

This paper provides a review of user selection algorithms for massive multiple-input multiple-output (MIMO) systems under the line-of-sight (LoS) propagation model. Although the LoS propagation is extremely important to some promising technologies, like in millimeter-wave communications, massive MIMO systems are rarely studied under this propagation model. This paper fills this gap by providing a comprehensive study encompassing several user selection algorithms, different linear precoders and simulation setups, and also considers the effect of partial channel state information (CSI). One important result is the existence of practical cases in which the LoS propagation model may lead to significant levels of interference among users within a cell; these cases are not satisfactorily addressed by the existing user selection algorithms. Motivated by this issue, a new user selection algorithm based on inter-channel interference (ICI) called ICI-based selection (ICIBS) is proposed. Unlike other techniques, the ICIBS accounts for the ICI in a global manner, thus yielding better results, especially in cases where there are many users interfering with each other. In such scenarios, simulation results show that when compared to the competing algorithms, the proposed approach provided an improvement of at least 10.9% in the maximum throughput and 7.7% in the 95%-probability throughput when half of the users were selected.

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.

Cooperative Rate-Splitting for Downlink Multiuser MISO Systems With Partial CSIT

Multiple antenna technology and cooperative relaying are two efficient methods to improve the capacity and reliability of wireless networks. By marrying cooperative relaying with a Rate-Splitting (RS) approach, Cooperative Rate-Splitting (CRS) shows certain benefits compared to several non-cooperative and cooperative schemes in multi-antenna Broadcast Channel (BC). In this paper, we further investigate the CRS with partial Channel State Information at the Transmitter (CSIT), as partial CSIT is an inevitable issue in practical downlink Multi-user Multiple Input Single Output (MU-MISO) systems. The proposed CRS structure includes multiple users, and one user could be selected as a relay to help the other users. With the flexible design, CRS leverages the benefits of user cooperation and relay (or user) selection for cooperative network, and also retains the superiority of RS for multi-antenna BC. Considering the Weighted Average Sum Rate (WASR) performance, we aim to solve the stochastic optimization that jointly involves the precoder design, the time resource allocation, and the relay selection issue. To address the above non-convex problem, a dedicated algorithm for CRS that exploits the Weighted Minimum Mean Square Error (WMMSE) approach and the Convex-Concave Procedure (CCP) is proposed, such that the WASR is maximized by jointly updating the precoding matrix and time allocation factor according to the incoming user channel state estimate. The proposed algorithm then enables us to solve the relay selection through an exhaustive search. Since the optimal relay selection algorithm has a high computational complexity when the number of users is large, we additionally provide a suboptimal heuristic algorithm to strike a balance between complexity and performance. Numerical results demonstrate that the proposed CRS scheme is capable of achieving a larger WASR than its non-cooperative counterpart and several existing beamforming schemes in a wide range of propagation conditions, and the heuristic algorithm achieves a performance close to the optimal one but with lower complexity.

Channel Estimation for Semi-Passive Reconfigurable Intelligent Surfaces With Enhanced Deep Residual Networks

Reconfigurable intelligent surface (RIS) is envisioned as an essential paradigm for realizing the sixth-generation networks, due to the use of low-cost reflecting elements for establishing programmable and favourable wireless environment. However, accurate channel estimation is a fundamental technical challenge for achieving large performance gains brought by RIS. To address this challenge, we first integrate a RIS with a small number of uniformly distributed active sensing devices, which are equipped with active radio frequency chains for acquiring partial channel state information (CSI). Then, by leveraging the rank-deficient structure of RIS channels, two practical residual neural networks, named single-scale enhanced deep residual (EDSR) and multi-scale enhanced deep residual (MDSR), are proposed to obtain accurate CSI, which can strike a balance between the system complexity and estimation performance. Simulation results reveal the cost-performance trade-off of the two proposed methods and unveil their superior performance compared with existing baseline schemes.

Adaptive Power Allocation for Wireless-Powered FD-NOMA System With Cooperation Versus Non-Cooperation

The paper studies a wireless-powered full-duplex non-orthogonal multiple access (NOMA) system with one base station and two groups of near and far users, where the near users can be regarded as the self-energy recycling relays to assist the far users. In particular, the locations of the near and far users are modeled as the uniformly distributed and homogeneous Poisson Point Process respectively. From the beneficial perspective of the near users, we propose a novel power allocation scheme, which can utilize the partial channel state information to switch the transmission mode dynamically between cooperative NOMA and non-cooperative NOMA. Further, since the users’ locations have a significant influence on our proposed power allocation scheme, three different user selection strategies namely random near user and random far user (RNRF), optimal near user and nearest far user (ONNF), and optimal near user and farthest far user (ONFF) are investigated to carry out NOMA pairing. Firstly, the RNRF allows all of users to communicate with the base station by a fair opportunity compared to the others strategies, and then a two-stage user selection method is proposed by considering the actual states of near users to determine an optimal near user who can maximize the outage performance of far user for ONNF and ONFF strategies. In addition, outage probabilities, diversity orders, and system throughputs are derived to characterize the performance of three strategies. Finally, numerical results are shown to verify that not only our proposed power allocation scheme can improve the quality of service of near users but also user selection strategies can fully exploit multi-user diversity in comparison to the existing work.