Complete Channel State Information Research Articles

Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI

Device-to-device (D2D) communication supports direct communications between nearby devices, which has potential to improve network capacity, spectrum efficiency and energy efficiency. Considering the high overhead to obtain the complete channel state information (CSI), we investigate resource allocation problems of D2D communication underlaying cellular networks with incomplete CSI to minimize the total power consumption. To deal with the challenge brought by incomplete CSI in estimating the instantaneous rates of D2D pairs (DPs), we consider two QoS metrics in terms of the expected rate and outage probability using statistical CSI. Based on that, we first investigate the energy-efficient power control problem for single cellular user (CU) and single DP sharing the same spectrum. With rigorous theoretical analysis of the intrinsic properties of CU rate and two QoS metrics, we design an optimal energy-efficient power control (EPO) algorithm for single CU and single DP. Using EPO as a building block, we then propose an energy-efficient resource allocation algorithm for multiple CUs and multiple DPs with incomplete CSI. Simulation results show that our algorithms consume the lowest powers compared with two baseline algorithms.

Orthonormal pilot-based channel estimation with low complexity phase shift optimization and coverage enhancement for IRS-assisted B5G communication

Beyond fifth-generation (B5G) technology features millimeter wave (mmWave) to ensure low latency and high capacity while enabling multiple-input and multiple-output (MIMO) facilities. MMWave signals are attenuated over short distances, resulting in a smaller coverage area. The B5G network relies on Intelligent Reflecting Surfaces (IRS) for reducing mmWave attenuation. IRS increases mmWave coverage and reduces obstruction risks by introducing phase shifts on incident beams. Complete channel state information (CSI) is required to estimate phase shifts in IRS and precoding. A novel orthonormal pilot matrix with a low-complexity channel estimation method is proposed in this context. The proposed channel estimation method achieved maximum accuracy with limited pilot sequences. A low-complexity phase shift optimization technique is also presented in this study using estimated channel information. On the other hand, the use of the desired number of reflective elements is highlighted to maximize coverage. On the basis of received signal strength and outage probability, two approaches are presented for determining the number of reflective elements. The derived closed-form expression of outage probability (OP) seems simple. It shows close agreement with the theoretical model. Results show that the proposed model provides better achievable rates than conventional IRS models.

Image super resolution based channel estimation for future wireless communication

In this paper, a novel image-based Deep Learning (DL) approach for channel estimation for future wireless communications is proposed. The time–frequency response of the fast-fading wireless channel is represented as a 2D image to estimate the unknown values of the channel response using known values at the pilot locations. With given images, both image Super-Resolution (SR) and image Denoising Network (DnN), termed as Super-Resolution and Denoising Network (SRDnN), are combined to estimate the wireless channel. To show the effectiveness, the proposed SRDnN is applied to Massive Multiple-Input Multiple-Output (mMIMO) with Non-Orthogonal Multiple Access (NOMA). The enhanced performances of SRDnN are quantified in terms of Mean Square Error (MSE) and Symbol Error Rate (SER). In addition, the influence of pilot numbers on SRDnN performance for next-generation mMIMO-NOMA networks is presented. The simulation results demonstrate that SRDnN is comparable to the level of Maximum Likelihood (ML) detection for both with and without complete Channel State Information (CSI) at the receiver with less number of pilots.

Energy Efficiency Resource Management for D2D-NOMA Enabled Network: A Dinkelbach Combined Twin Delayed Deterministic Policy Gradient Approach

In this paper, a joint cluster association and power allocation resource management scheme is proposed in a D2D-NOMA-enabled heterogeneous network. The target is to maximize users' long-term cumulative energy efficiency. A novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -times iteration method based on matching theory is constructed to handle the NOMA cluster association, which is formulated as the perfect matching of a weighted bipartite graph. Considering it is difficult to obtain the complete instantaneous channel state information, we offer a deep reinforcement learning (DRL) framework, which guarantees the feasibility of DRL methods for the NP-hard power allocation. Meanwhile, an optimal reward function is designed to enhance the training and interacting efficiency. Based on the proposed DRL framework, the Dinkelbach-TD3 power allocation approach is proposed to shrink the action range of twin delayed deep deterministic policy gradient (TD3) with the fractional programming method Dinkelbach, which elaborately increases the training efficiency and the precision of the DRL methods. Simulation results demonstrate that the proposed DRL framework has significant energy efficiency performance improvement.

Transmit Antenna Subset Selection in Cooperative SM-MIMO Vehicular System

This paper proposes a transmit antenna subset selection (TASS) in decode-and-forward (DF) cooperative spatial modulation multiple-input multiple-output (CSM-MIMO) vehicular system to enhance the communication performance. Specifically, the first-order autoregressive (FOAR) process is taken to model the time-varying vehicular fading channels. The transmit antenna (TA) subsets at the source and the relay that maximize the minimum squared Euclidean distance (MSED) can be respectively selected, which requires no complete channel state information (CSI). A closed-form analytical average bit error rate (ABER) of the CSM-MIMO vehicular system with the proposed TASS is derived. Numerical results demonstrate the correctness of the analytical ABER performance. The proposed TASS in the CSM-MIMO vehicular system achieves superior ABER performance than the existing CSM-MIMO system and the traditional TASS schemes.

Robust Beamforming Design for IRS-Aided Secure Communication Systems Under Complete Imperfect CSI

We investigate the performance of an intelligent reflecting surface (IRS) aided secure communication system which operates in a complete imperfect channel state information (CSI) regime. By jointly considering the imperfect CSI of both IRS-Bob and IRS-Eve channels, we propose a robust transmission scheme which is based upon a joint optimization of the continuous transmit beamforming at Alice and the discrete phase shifts at the IRS. To deal with this mixed integer non-convex optimization problem, novel lower- and upper-bounding techniques are used to transform it in a more tractable form for which a solution based upon the derivation of a quantization-based alternative optimization (AO) algorithm is presented. More specifically, on the one hand, a successive convex approximation (SCA) algorithm is proposed to optimize the continuous transmit beamforming. On the other hand, by applying a SCA, penalty convex-concave procedure (PCCP), and quantization-based algorithm, the discrete phase shifts are obtained. Various performance evaluation results obtained by means of computer simulations have shown that the proposed robust transmission scheme guarantees a minimum quality of service (QoS) while simultaneously limits Eve's maximum eavesdropping rate.

Coverage Analysis of Hybrid RF/THz Networks With Best Relay Selection

Utilizing terahertz (THz) transmission to enhance coverage has proven various benefits compared to traditional radio frequency (RF) counterparts. This letter proposes a dual-hop decode-and-forward (DF) routing protocol in a hybrid RF and THz relay network named hybrid relay selection (HRS). The coverage probability of the HRS protocol is derived. The HRS protocol prioritizes THz relays for higher data rates or short source-destination distances; and RF relays for lower data rates or large source-destination distances. The proposed HRS protocol offers nearly the same performance as the optimal selection protocol, which requires complete instantaneous channel state information (CSI) of all the nodes.

RIS-Assisted UAV for Fresh Data Collection in 3D Urban Environments: A Deep Reinforcement Learning Approach

Dispatching flexible unmanned aerial vehicles (UAVs) to collect data from distributed Internet-of-Things devices (IoTDs) is expected to be a promising technology to support time-critical applications. However, in urban environments, the communication links between the UAV and IoTDs are prone to be frequently blocked by buildings, which severely impairs the freshness of information collected by the UAV. Thus, how to overcome urban building blockages and ensure fresh data collection is quite important but neglected in existing works. In this paper, for keeping the information fresh, we propose to utilize the reconfigurable intelligent surface (RIS) to assist the UAV in mitigating signal propagation impairments caused by building blockages. The formulated optimization problem is minimizing the age of information (AoI) of all IoTDs by jointly optimizing the UAV trajectory, IoTD scheduling and discrete phase shifts of the RIS. It is a mixed-integer non-convex problem as well as lacking the complete channel state information (CSI), thus using conventional optimization methods is intractable. To address this issue, we present an effective and robust deep reinforcement learning (DRL)-based scheme called “SAC-AO-RIS,” where a soft actor-critic (SAC) algorithm with recent-prioritized experience replay is designed for learning highly stable policies of UAV trajectory and IoTD scheduling, and an alternating optimization (AO) algorithm is leveraged for solving RIS phase shifts. Finally, simulation results demonstrate the effectiveness and superiority of our proposed scheme compared with other baseline approaches.

Federated Learning via Over-the-Air Computation With Statistical Channel State Information

Federated learning (FL) is a popular distributed learning paradigm, in which a global model at a server learns private data of clients without data shared among clients or the server. In this paper, we consider FL over a noisy fading multiple access channel (MAC) via over-the-air computation (AirComp). Benefiting from waveform-superposition propriety of wireless signals, AirComp is able to achieve fast aggregations in FL and improve spectral efficiency. However, most of the schemes exploiting AirComp require intensive channel estimations as the demands from precoders, which results in considerable communication overheads. For this reason, we propose two novel FL schemes with statistical channel state information (FL-SCSI-A and FL-SCSI-B) to reduce the efforts required by channel estimations. In FL-SCSI-A, precoders adjust phases of transmitted signals with phases of instant channel state information (CSI), and scale transmitted signal powers based on statistical CSI. The precoder design has the following two advantages. First, phases of instant CSI can be easier to estimate than complete instant CSI. Second, since clients only need to estimate phases of their own instant CSI (instead of CSI of all clients), with channel reciprocity, this can be easily achieved by letting the server broadcast pilots to all clients. The server in FL-SCSI-A is also efficient. It only needs to estimate the sum of channel gains of all clients, which can be easily achieved by letting clients transmit pilots simultaneously. To further reduce the communication overhead, FL-SCSI-B is proposed. The precoders in FL-SCSI-B are similar to FL-SCSI-A, while the server does not require any knowledge of instant CSI, which reduces the demands of channel estimations. For both schemes, we prove that the distortion caused by the noisy fading MAC is bounded, and the convergences of the learning processes are guaranteed for strongly smooth losses with heterogeneous data assumptions. Experimental results show that the proposed schemes perform better than benchmark schemes while reducing efforts required by channel estimation.

Deep Learning-Assisted Secure UAV-Relaying Networks With Channel Uncertainties

Unmanned aerial vehicles (UAVs) play a critical role for 5 G/B5G communications. In this paper, we investigate the physical layer security issue for ground transmissions with the help of a UAV. Specifically, we consider the scenario without direct transmissions and employ the UAV as a mobile relay to amplify and forward the confidential information. Meanwhile, the receiver transmits cooperative jamming attempting to disrupt eavesdropping. We investigate the UAV deployment and receiver jamming strategy jointly for the optimized secrecy rate, while considering the channel uncertainties. In particular, for the case of complete channel state information (CSI), the jamming power is obtained by bi-section search, and we propose a data-trained deep neural network (DNN) to approximate the optimal UAV deployment. Meanwhile, for the case with partial CSI, we design a deep <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> network (DQN) to learn the optimal UAV deployment, due to the increased complexity to determine the jamming strategy with channel statistics. Simulation results demonstrate that both the learned DQN and trained DNN model effectively approximate the optimal deployment of the UAV. Moreover, compared with the baselines, our proposal significantly improves the security performance of legitimate transmissions.

When mmWave High-Speed Railway Networks Meet Reconfigurable Intelligent Surface: A Deep Reinforcement Learning Method

Reconfigurable intelligent surface (RIS) is an efficient and promising way to enhance millimeter-wave (mmWave) high-speed railway (HSR) network resilience. However, due to the harsh nature of HSR environment and the unpredictable hardware damage of RIS, it is challenging to perceive accurate and complete channel state information (CSI). To tackle the challenge, this letter proposes a novel deep reinforcement learning framework to design the base station (BS) transmission beamforming and RIS phase shifts for the RIS aided mmWave HSR networks, which combines long short-term memory (LSTM) and deep deterministic policy gradient (DDPG), termed as LSTM-DDPG. The simulation results demonstrate that the proposed LSTM-DDPG scheme outperforms the benchmark schemes in terms of spectral efficiency with comparatively low execution time, which makes real-time decision-making truly viable in the dynamic HSR network.

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.

Cluster-Based Radio Resource Management in Dynamic Vehicular Networks

With the rapid growth of traffic demand for vehicular data transmissions, the limited cellular band becomes a constraint to meet the needs of all cellular-vehicle-to-everything (C-V2X) users. In this paper, we investigate an efficient clustering-based spectrum resource management scheme for dynamic heterogeneous vehicular networks. Due to the frequent changes of channel state information (CSI) caused by high mobility of vehicles, the traditional radio resource management scheme based on complete CSI is no longer applicable. To solve this problem, we consider different quality of service (QoS) requirements for vehicular communications and propose an optimization problem for C-V2X networks. The proposed dynamic vehicular optimization strategy is carried out on the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, which can construct a more realistic two-way lane in actual communication environments. We also propose a low-complexity vehicle matching algorithm based on large-scale fading to improve the stability of communication links. The desirable performance is confirmed by computer simulations.

Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications

We have studied massive MIMO hybrid beamforming (HBF) for millimeter-wave (mmWave) communications, where the transceivers only have a few radio frequency chain (RFC) numbers compared to the number of antenna elements. We propose a hybrid beamforming design to improve the system’s spectral, hardware, and computational efficiencies, where finding the precoding and combining matrices are formulated as optimization problems with practical constraints. The series of analog phase shifters creates a unit modulus constraint, making this problem non-convex and subsequently incurring unaffordable computational complexity. Advanced deep reinforcement learning techniques effectively handle non-convex problems in many domains; therefore, we have transformed this non-convex hybrid beamforming optimization problem using a reinforcement learning framework. These frameworks are solved using advanced deep reinforcement learning techniques implemented with experience replay schemes to maximize the spectral and learning efficiencies in highly uncertain wireless environments. We developed a twin-delayed deep deterministic (TD3) policy gradient-based hybrid beamforming scheme to overcome Q-learning’s substantial overestimation. We assumed a complete channel state information (CSI) to design our beamformers and then challenged this assumption by proposing a deep reinforcement learning-based channel estimation method. We reduced hybrid beamforming complexity using soft target double deep Q-learning to exploit mmWave channel sparsity. This method allowed us to construct the analog precoder by selecting channel dominant paths. We have demonstrated that the proposed approaches improve the system’s spectral and learning efficiencies compared to prior studies. We have also demonstrated that deep reinforcement learning is a versatile technique that can unleash the power of massive MIMO hybrid beamforming in mmWave systems for next-generation wireless communication.

Contract Theory-Based Incentive Mechanism for Full Duplex Cooperative NOMA with SWIPT Communication Networks.

Cooperative Non-Orthogonal Multiple Access (NOMA) with Simultaneous Wireless Information and Power Transfer (SWIPT) communication can not only effectively improve the spectrum efficiency and energy efficiency of wireless networks but also extend their coverage. An important design issue is to incentivize a full duplex (FD) relaying center user to participate in the cooperative process and achieve a win–win situation for both the base station (BS) and the center user. Some private information of the center users are hidden from the BS in the network. A contract theory-based incentive mechanism under this asymmetric information scenario is applied to incentivize the center user to join the cooperative communication to maximize the BS’s benefit utility and to guarantee the center user’s expected payoff. In this work, we propose a matching theory-based Gale–Shapley algorithm to obtain the optimal strategy with low computation complexity in the multi-user pairing scenario. Simulation results indicate that the network performance of the proposed FD cooperative NOMA and SWIPT communication is much better than the conventional NOMA communication, and the benefit utility of the BS with the stable match strategy is nearly close to the multi-user pairing scenario with complete channel state information (CSI), while the center users get the satisfied expected payoffs.

Deep Learning-Aided Distributed Transmit Power Control for Underlay Cognitive Radio Network

In this paper, we investigate deep learning-aided distributed transmit power control in the context of an underlay cognitive radio network (CRN). In the proposed scheme, the fully distributed transmit power control strategy of secondary users (SUs) is learned by means of a distributed deep neural network (DNN) structure in an unsupervised manner, such that the average spectral efficiency (SE) of the SUs is maximized whilst allowing the interference on primary users (PUs) to be regulated properly. Unlike previous centralized DNN-based strategies that require complete channel state information (CSI) to optimally determine the transmit power of SU transceiver pairs (TPs), in our proposed scheme, each SU TP determines its own transmit power based solely on its local CSI. Our simulation results verify that the proposed scheme can achieve a near-optimal SE comparable with a centralized DNN-based scheme, with a reduced computation time and no signaling overhead.

A reinforcement learning approach to queue-aware scheduling in full-duplex wireless networks

Full-duplex communications promise to double the throughput of a wireless network, so long as the resulting interferences can be combated. Nonetheless, already dealing with the intricacy of determining base station-to-user radio channels, full duplex wireless networks need additional information on the channels in between all the user equipment. This information is necessary to determine user radio conditions, and thereafter efficiently allocate resources. A signaling overhead is likely to burden the user equipment, which already lack any methods to estimate and convey such user-to-user channel states. In this paper, we aim to circumvent the complexities and requirements of traditional scheduling techniques by introducing a reinforcement learning based scheduling algorithm for full-duplex wireless networks. This scheduling approach does not need to estimate user-to-user channels, and rather learns how to best allocate the network’s radio resources. We show that our proposal can match scheduling with complete channel state information in terms of user equipment throughput, and that it performs well under multiple testing scheduling scenarios: increased user equipment numbers, randomized user equipment demand, and user equipment clustering among others.

Multipair Relaying With Space-Constrained Large-Scale MIMO Arrays: Spectral and Energy Efficiency Analysis With Incomplete CSI

In this paper, we study a multi-pair two-way large-scale multiple-input multiple-output (MIMO) decode-and-forward relay system. Multiple single-antenna user pairs exchange information via a shared relay working at half-duplex. The proposed scenario considers a practical case where an increasing number of antennas is deployed in a fixed physical space, giving rise to a trade-off between antenna gain and spatial correlation. The channel is assumed imperfectly known, and the relay employs linear processing methods. We study the large-scale approximations of the sum spectral efficiency (SE) and investigate the energy efficiency (EE) with a practical power consumption model when the number of relay antennas becomes large. We demonstrate the impact of the relay antenna number and spatial correlation with reducing inter-antenna distance on the EE performance. We exploit the increasing spatial correlation to allow an incomplete channel state information (CSI) acquisition where explicit CSI is acquired only for a subset of antennas. Our analytical derivations and numerical results show that applying the incomplete CSI strategy in the proposed system can improve the EE against complete CSI systems while maintaining the average SE performance.

Analysis and Optimization of an Intelligent Reflecting Surface-Assisted System With Interference

In this paper, we study an intelligent reflecting surface (IRS)-assisted system where a multi-antenna base station (BS) serves a single-antenna user with the help of a multi-element IRS in the presence of interference generated by a multi-antenna BS serving its own single-antenna user. The signal and interference links via the IRS are modeled with Rician fading. To reduce phase adjustment cost, we adopt quasi-static phase shift design where the phase shifts do not change with the instantaneous channel state information (CSI). We investigate two cases of CSI at the BSs, namely, the instantaneous CSI case and the statistical CSI case, and apply Maximum Ratio Transmission (MRT) based on the complete CSI and the CSI of the Line-of-sight (LoS) components, respectively. Different costs on channel estimation and beamforming adjustment are incurred in the two CSI cases. First, we obtain a tractable expression of the average rate in the instantaneous CSI case and a tractable expression of the ergodic rate in the statistical CSI case. We also provide sufficient conditions for the average rate in the instantaneous CSI case to surpass the ergodic rate in the statistical CSI case, at any phase shifts. Then, we maximize the average rate and ergodic rate, both with respect to the phase shifts, leading to two non-convex optimization problems. For each problem, we obtain a globally optimal solution under certain system parameters, and propose an iterative algorithm based on parallel coordinate descent (PCD) to obtain a stationary point under arbitrary system parameters. Next, in each CSI case, we provide sufficient conditions under which the optimal quasi-static phase shift design is beneficial, compared to the system without IRS. Finally, we numerically verify the analytical results and demonstrate notable gains of the proposal solutions over existing ones.

Hybrid Offline-Online Design for UAV-Enabled Data Harvesting in Probabilistic LoS Channels

This paper considers an unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) in urban areas, where a UAV is deployed to collect data from distributed sensor nodes (SNs) within a given duration. To characterize the occasional building blockage between the UAV and SNs, we construct the probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using the combined simulation and data regression method, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. We assume that only the knowledge of SNs’ locations and the probabilistic LoS channel model is known a priori , while the UAV can obtain the instantaneous LoS/Non-LoS channel state information (CSI) with the SNs in real time along its flight. Our objective is to maximize the minimum (average) data collection rate from all the SNs for the UAV. To this end, we formulate a new rate maximization problem by jointly optimizing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Although the optimal solution is intractable due to the lack of complete UAV-SNs CSI, we propose in this paper a novel and general design method, called hybrid offline-online optimization, to obtain a suboptimal solution to it, by leveraging both the statistical and real-time CSI. Essentially, our proposed method decouples the joint design of UAV trajectory and communication scheduling into two phases: namely, an offline phase that determines the UAV path prior to its flight based on the probabilistic LoS channel model, followed by an online phase that adaptively adjusts the UAV flying speeds along the offline optimized path as well as communication scheduling based on the instantaneous UAV-SNs CSI and SNs’ individual amounts of data received accumulatively. Extensive simulation results are provided to show the significant rate performance improvement of our proposed design as compared to various benchmark schemes.