Unknown Channel State Information Research Articles

Deep Reinforcement Learning-Based Resource Management in Maritime Communication Systems.

With the growing maritime economy, ensuring the quality of communication for maritime users has become imperative. The maritime communication system based on nearshore base stations enhances the communication rate of maritime users through dynamic resource allocation. A virtual queue-based deep reinforcement learning beam allocation scheme is proposed in this paper, aiming to maximize the communication rate. More particularly, to reduce the complexity of resource management, we employ a grid-based method to discretize the maritime environment. For the combinatorial optimization problem of grid and beam allocation under unknown channel state information, we model it as a sequential decision process of resource allocation. The nearshore base station is modeled as a learning agent, continuously interacting with the environment to optimize beam allocation schemes using deep reinforcement learning techniques. Furthermore, we guarantee that grids with poor channel state information can be serviced through the virtual queue method. Finally, the simulation results provided show that our proposed beam allocation scheme is beneficial in terms of increasing the communication rate.

UAV Trajectory and Multi-User Beamforming Optimization for Clustered Users Against Passive Eavesdropping Attacks With Unknown CSI

This paper tackles the fundamental passive eavesdropping problem in modern wireless communications in which the location and the channel state information (CSI) of the attackers are unknown. In this regard, we propose deploying an unmanned aerial vehicle (UAV) that serves as a mobile aerial relay (AR) to help ground base station (GBS) support a subset of vulnerable users. More precisely, our solution (1) clusters the single-antenna users in two groups to be either served by the GBS directly or via the AR, (2) employs optimal multi-user beamforming to the directly served users, and (3) optimizes the AR's 3D position, its multi-user beamforming matrix and transmit powers by combining closed-form solutions with machine learning techniques. Specifically, we design a plain beamforming and power optimization combined with a deep reinforcement learning (DRL) algorithm for an AR to optimize its trajectory for the security maximization of the served users. Numerical results show that the multi-user multiple input, single output (MU-MISO) system split between a GBS and an AR with optimized transmission parameters without knowledge of the eavesdropping channels achieves high secrecy capacities that scale well with increasing the number of users.

Hybrid Beamforming for Secure Transmission of Massive MIMO UAV Communication Networks

The powerful line-of-sight (LoS) characteristic between unmanned aerial vehicles (UAV) and terrestrial nodes could be leveraged by large-scale antenna arrays to significantly increase transmission rates, while the problem of secure transmission is emerging. Compared with fully digital beamforming, hybrid beamforming (HBF) could meet a compromise between cost and performance, which is precisely optimized in this article to maximize the secrecy rate of massive multiple-input–multiple-output (MIMO) UAV communication network. At first, we propose a secure HBF framework based on generalized eigenvalue decomposition and singular value decomposition. After that, considering the difficulty in directly solving the HBF optimization problem, an iterative algorithm based on successive convex approximation is developed for perfectly known channel state information (CSI). Furthermore, while CSI is imperfect, a neural network is constructed and trained to learn the statistical characteristics of wireless channels and complete the design of hybrid beamformer and combiner simultaneously. Performance validation and beampattern of the proposed HBF methods are given by extensive simulation results, which demonstrate the proposed secure HBF framework could considerably improve secrecy rate, and the deep learning-based HBF method possesses a positive average secrecy rate even with unknown eavesdropper's CSI.

Beamforming Design for In-Band Full-Duplex Multi-Cell Multi-User MIMO Networks With Global and Local CSI

This paper investigates beamforming techniques for in-band full-duplex multi-cell multi-user (IBFD-MCMU) wireless networks considering hardware impairments (HWIs), channel uncertainty, and limited channel state information (CSI). For the global CSI scenario, we first enhance zero-forcing (ZF), maximum ratio transmission and combining (MRTC), and minimum mean-squared error (MMSE) beamforming to be compatible with multi-antenna users and IBFD base stations. Then, we investigate beamforming schemes for the local CSI assumption where only intra-cell channel knowledge is fully available at the base stations, and inter-cell channels are known statistically due to the limited training resources. With these limitations, two MMSE-based methods are proposed, which regard unknown CSI as random instances (i.e., eMMSE-RI) and noise (i.e., eMMSE-N). We explore the self-interference cancellation (SIC) capability of beamformers and evaluate the performance of these methods in 3GPP scenarios. Numerical results reveal that the enhanced MMSE beamforming can achieve the desired IBFD with low analog SIC depth, inspiring a low-cost IBFD transceiver design. The practical imperfections (e.g., transceiver HWIs, channel uncertainty, limited CSI) decrease the achievable sum rate but do not reduce the IBFD gain. With CSI limitations, eMMSE-RI outperforms eMMSE-N for microcells, where the probability of the presence of LOS is high, whereas eMMSE-N performs better when cell size is enlarged.

Next-Generation URLLC With Massive Devices: A Unified Semi-Blind Detection Framework for Sourced and Unsourced Random Access

This paper proposes a unified semi-blind detection framework for sourced and unsourced random access (RA), which enables next-generation ultra-reliable low-latency communications (URLLC) with a massive number of devices. Specifically, the active devices transmit their uplink access signals in a grant-free manner to realize ultra-low access latency. Meanwhile, the base station aims to achieve ultra-reliable data detection under severe inter-device interference without exploiting explicit channel state information (CSI). We first propose an efficient transmitter design, where a small amount of reference information (RI) is embedded in the access signal to resolve the inherent ambiguities incurred by the unknown CSI. At the receiver, we further develop a successive interference cancellation-based semi-blind detection scheme, where a bilinear generalized approximate message passing algorithm is utilized for joint channel and signal estimation (JCSE), while the embedded RI is exploited for ambiguity elimination. Particularly, a rank selection approach and a RI-aided initialization strategy are incorporated to reduce the algorithmic computational complexity and to enhance the JCSE reliability, respectively. Besides, four enabling techniques are integrated to satisfy the stringent latency and reliability requirements of massive URLLC. Numerical results demonstrate that the proposed semi-blind detection framework offers a better scalability-latency-reliability tradeoff than the state-of-the-art detection schemes dedicated to sourced or unsourced RA.

Intelligent Reflecting Surface Secure Backscatter Communication Without Eavesdropping CSI

This letter presents an intelligent reflecting surface secure backscatter communication system. In this system, an IRS, as a backscatter device, leverages the signals from a power beacon (PB) as carriers to send confidential messages to a legitimate user. Meanwhile, an eavesdropper with unknown channel state information (CSI) attempts to intercept the messages. A power minimization scheme using the block coordinate descent and the semidefinite program methods is developed to realize secure transmission. This scheme jointly optimizes the PB’s beamformer and the IRS’s reflection coefficient to elevate the secrecy rate. Simulation results demonstrate that the optimization scheme can achieve secure communication in the proposed system.

Secured End-to-End FSO-VLC-Based IoT Network With Randomly Positioned VLC: Known and Unknown CSI

In this article, we determine the secrecy performance of a dual-hop hybrid free space optics (FSO)-visible light communication (VLC) system with a randomly located legitimate Internet of Things (IoT)-based receiver in the presence of two distinct eavesdroppers (EDs). We consider one FSO-based ED located near the relay, wiretapping the FSO link. However, another ED wiretaps the information from the multiple VLC sources embedded on the ceiling of the room. We consider the three different scenarios for VLC-based ED: 1) the effect of uniformly distributed ED in the absence of any secured zone in the VLC coverage area; 2) the impact of uniformly distributed ED in the presence of VLC secured area; and 3) the effect of random waypoint (RWP) distributed ED on the system’s security. The secrecy performance of the system is evaluated based on known and unknown instantaneous channel state information (CSI) of EDs. Furthermore, the closed form expression for secrecy outage probability (SOP) in terms of Meijer’s-G and Gauss hypergeometric functions is derived. In order to have a better insight into the system’s performance, analytical results are expressed asymptotically in the high signal-to-noise ratio (SNR) regime. Following that, Monte Carlo simulations are done to validate the theoretical results. Additionally, a secrecy threshold is obtained in order to comprehend the system’s security and reliability.

Artificial Dust Based Attack Modelling: A Threat to the Security of Next Generation WCN

This paper introduces a systematic and novel mechanism for devising a security attack in the WCN (Wireless Communication Network). The proposed model involves the implementation of the AD (Artificial Dust) by the intruder, followed by the execution of the HD (Half-Duplex) attack. The communication network is based on the deployment of urban and rural scenarios with an unknown CSI (Channel State Information). Depending on the achieved path loss based on the distance of the user from the BS, the user with the highest path loss is particularized for the attack. The formulation of AD divulges the increased susceptibilities of the secure network specifically for the selected legitimate user. The parameter of visibility defines the amount of AD present in the communication channel. Based on the enumerated attenuation created by the artificial dust, the parameter of secrecy rate is evaluated with varying distance of the user from the BS and the operating frequency. Furthermore, the proposed scheme of the HD attack is initiated by the intruder at the specified valid user. The strategy of the attack focuses on the continuous monitor of the uplink and attempts the spoofing attack on the downlink wherein the allocation of the resources takes place. The efficacy of the proposed approach is corroborated through the examination of simulation results. The assessment of the proposed mechanism highlights notable characteristics as compared to the conventional methodology of the FD (Full-Duplex) attack.

Proactive Eavesdropping in Massive MIMO-OFDM Systems via Deep Reinforcement Learning

It is challenging to monitor suspicious links in massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with directional beamforming. When the monitor and suspicious user are not in the coverage of the same beam, to implement legitimate monitoring, we propose the proactive eavesdropping scheme in which the monitor acts as a spoofing relay to realize beam misleading and data eavesdropping. In the phase of beam sweep, the beam misleading algorithm induces the transmitter to choose the beam which is beneficial to the monitor by optimizing the relay precoding matrices. In the phase of data transmission, the data eavesdropping algorithm elevates the monitoring rate by optimizing the relay power split factors and power gain factors of all subcarriers. Due to the unknown channel state information between suspicious pairs, we propose to find the optimal precoders and power split factors by the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The simulation results verify the effectiveness and superiority of the proposed eavesdropping scheme.

Secure Transmit Beamforming for Radar-Communication System Without Eavesdropper CSI

This article studies the transmit beamforming security in the dual-functional radar-communication (DFRC) system, where the radar beam also serves as the artificial jamming to suppress potential eavesdropper channels. Unlike previous researches, which assumed that the lone target might be a potential eavesdropper on the adversary side, this paper analyses a more generic scenario in which the eavesdroppers’ channel state information (CSI) is unknown. The eavesdropping rate should be limited to guarantee transmission confidentiality, which is intractable owing to the unknown CSI. To address this issue, we present the long-term eavesdropping signal-to-interference-plus-noise ratio (SINR) as a more tractable security metric. Based on the long-term eavesdropping SINR, the sum rate maximization (SRM) and jamming power maximization (JPM) transmission scheme are investigated. For the SRM scheme, we propose a zero-forcing (ZF) suboptimal algorithm to solve the non-convex fractional programming (FP) problem, and for the JPM scheme, the resultant problem is solved via the semidefinite relaxation (SDR) technique. Finally, numerical results validate the effectiveness of the proposed beamforming approaches.

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.

Relay selection scheme based on deep reinforcement learning in wireless sensor networks

Cooperative communication technology has realized the enhancement in the wireless communication system’s spectrum utilization rate without resorting to any additional equipment; additionally, it ensures system reliability in transmission, increasingly becoming a research focus within the sphere of wireless sensor networks (WSNs). Since the selection of relay is crucial to cooperative communication technology, this paper proposes two different relay selection schemes subject to deep reinforcement learning (DRL), in response to the issues in WSNs with relay selection in cooperative communications, which can be summarized as the Deep-Q-Network Based Relay Selection Scheme (DQN-RSS), as well as the Proximal Policy Optimization Based Relay Selection Scheme (PPO-RSS); it further compared the commonly used Q-learning relay selection scheme (Q-RSS) with random relay selection scheme. First, the cooperative communication process in WSNs is modeled as a Markov decision process, and DRL algorithm is trained in accordance with the outage probability, as well as mutual information (MI). Under the condition of unknown instantaneous channel state information (CSI), the best relay is adaptively selected from multiple candidate relays. Thereafter, in view of the slow convergence speed of Q-RSS in high-dimensional state space, the DRL algorithm is used to accelerate the convergence. In particular, we employ DRL algorithm to deal with high-dimensional state space while speeding up learning. The experimental results reveal that under the same conditions, the random relay selection scheme always has the worst performance. And compared to Q-RSS, the two relay selection schemes designed in this paper greatly reduce the number of iterations and speed up the convergence speed, thereby reducing the computational complexity and overhead of the source node selecting the best relay strategy. In addition, the two relay selection schemes designed and raised in this paper are featured by lower-level outage probability with lower-level energy consumption and larger system capacity. In particular, PPO-RSS has higher reliability and practicability.

A resource allocation scheme for D2D communications with unknown channel state information

A resource allocation scheme for D2D communications with unknown channel state information

Security Performance Analysis of a NOMA-Assisted Underwater VLC System Under Imprecise Channel Estimations

The accurate estimation of underwater Visible Light Communication (VLC) channel conditions is challenging due to its widespread attenuation and scattering effects. The channel attenuation is a linear function of frequency and causes exponential signal power loss whereas due to the scattering effect, numerous photons are statistically generated as light beams strike water molecules and there arise security concerns. Assuming realistic underwater conditions, this paper investigates the security performance of a typical Non-Orthogonal Multiple Access (NOMA)-assisted underwater VLC system. It consists of a Floating Vehicle Transmitter (FVT), equipped with multiple Light Emitting Diodes (LEDs) to transmit the signal to two legitimate near-end and far-end Underwater Vehicles (UVs) in presence of an active/passive eavesdropper. The Channel State Information (CSI) of each transmitting link is estimated with the use of a Minimum Mean Square Error (MMSE) technique. Furthermore, we propose a LED selection mechanism to select an LED that can achieve the highest secrecy rate defined under the constraints of known and unknown CSI of legitimate and/or eavesdropping links. Using the Successive Interference Cancellation (SIC) technique, a novel closed-form secrecy outage probability expressions for the conventional single-LED and multi-LED NOMA-VLC links for both known and unknown CSI scenarios is derived. The security performance of the proposed multi-LED NOMA-VLC system is compared with the conventional single-LED NOMA-VLC system under the effect of air bubbles for both fresh and salty water. Finally, we verify the validity of the numerical results through Monte-carlo simulation analysis.

Robust Resource Allocation for Vehicular Communications With Imperfect CSI

The resource allocation in vehicle-to-everything (V2X) communications face a great challenge for satisfying the heterogeneous quality of service (QoS) requirements of the ultra-reliable safety-related services and the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. In this paper, we first consider an optimistic scenario where the distribution of uncertain channel state information (CSI) can be deterministic and accurately known at the eNB. Then, a low-complexity resource allocation approach is developed, in which the probabilistic QoS constraint of V2V is transformed into a computable optimization constraint. To deal with the scenario with unknown uncertain CSI distribution, we develop a distributionally robust resource allocation approach for converting the intractable chance constraint of V2V into a deterministic semidefinite constraint based only on the first- and second-order moments of uncertain CSI. For alleviating the conservatism of above approach, a support-based distributionally robust resource allocation approach is developed to tighten the semidefinite constraint by utilizing the support information of uncertain CSI. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

Optimization of Energy Efficiency for Uplink Wireless Information and Downlink Power Transfer System with Imperfect CSI

As an emerging paradigm, supplying power by radio frequency signal has been a key technology for the wireless powered communication network (WPCN) to prolong the lifetime. This paper considers a multiple input multiple output (MIMO) system where users are charged only by one source. The source is equipped with multiple antennas while each user with one antenna. Besides receiving information as the traditional way, the source has the capacity to transfer energy with beamforming, which can be harvested by users to store for information transmission in the later. However, the unknown channel state information (CSI), low energy efficiency, and various demands of transmitting volume jointly raise inaccurate, wasteful, and flexible conditions in transmitting design. On the other hand, energy and spectrum efficient solutions are indispensable to the success of Internet of Things (IoT). In this case, we put forward a novel design of downlink energy transfer, uplink information transmission, and channel estimation to achieve a practical efficient transmission. By jointly optimizing the source antenna number, power allocation, energy beamforming vectors, and each phase time of channel estimation, energy harvest, and information transmission, we aim to achieve the optimized system energy efficiency with constraints of signal-to-noise ratio (SNR), data transmission volume, and transmitting power. Based on fractional programming and Lagrangian dual functions, we also put forward a distributed iterative algorithm to solve the formulated problem optimally. Simulation results verify the convergence of our proposed algorithm and illustrate the relationship between variables of antenna number, data volume requirement, pathloss factor and system performance of sum-throughput, energy efficiency, and user fairness. Our proposed transmitting design can achieve the optimized energy efficiency, whose upper bound is improved by appropriate massive antenna employment.

Polar-Coded Non-Coherent Communication

A polar-coded transmission (PCT) scheme with joint channel estimation and decoding is proposed for channels with unknown channel state information (CSI). The CSI is estimated via successive cancellation (SC) decoding and the constraints imposed by the frozen bits. SC list decoding with an outer code improves performance, including resolving a phase ambiguity when using quadrature phase-shift keying (QPSK) and Gray labeling. Simulations with 5G polar codes and QPSK show gains of up to 2 dB at a frame error rate (FER) of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> over pilot-assisted transmission for various non-coherent models. Moreover, PCT performs within a few tenths of a dB to a coherent receiver with perfect CSI. For Rayleigh block-fading channels, PCT outperforms an FER upper bound based on random coding and operates within one dB of a lower bound.

Artificial Noise-Aided Secure Relay Communication With Unknown Channel Knowledge of Eavesdropper

In this article, a new relay-aided secure communication system is investigated, where a transmitter sends signals to a destination via an amplify-and-forward (AF) relay in the presence of an eavesdropper. We consider a general system configuration, where the source, relay, destination, and eavesdropper are all equipped with multiple antennas. In the practical scenarios of unknown eavesdropper's channel state information (CSI) and uncertainty of the eavesdropper's location, we aim to maximize the expected value of the system secrecy rate over the presumed distribution of the eavesdropper's channels, by exploiting the artificial noise (AN) transmitted by the source and relay nodes. The system design issue is formulated as a nonconvex stochastic optimization problem with a source transmission power constraint and a nonconvex relay transmission power constraint. A novel computational method is proposed to solve this challenging problem. The new method is developed based on an exact penalty function method together with a parallel stochastic decomposition algorithm. Numerical simulations are performed to study the effectiveness of the proposed scheme at various locations of the eavesdropper. Simulation results show that for most cases, secure communication can be achieved without the CSI knowledge of eavesdropper's channels, and the achievable secrecy rate follows the trend of a benchmark system where the eavesdropper's full CSI is available. In particular, the achievable system secrecy rate increases with the number of antennas at the legitimate users. Moreover, the optimal power allocated for the transmission of the AN increases with the system signal-to-noise ratio. The proposed computational method achieves a higher system secrecy rate than a conventional penalty function based approach.

Energy Efficient Artificial Noise-Aided Precoding Designs for Secured Visible Light Communication Systems

Physical layer security (PLS) has recently gained a lot of attention in the research and development of visible light communication (VLC). In this article, we study the designs of PLS in VLC systems in the presence of multiple unauthorized users (i.e. eavesdroppers) using artificial noise (AN)-aided precoding. The design objective focuses on minimizing the total transmit power subject to specific constraints on the signal-to-interference-plus-noise ratios (SINRs) of the legitimate and unauthorized users. In particular, two design approaches are investigated considering the availability of unauthorized users' channel state information (CSI) at the transmitter. In the case of unknown CSI, the AN is constructed to lie on the null-space of the legitimate user's channel. The design problem is convex, thus, can be effectively solved. When the CSI is available, the design additionally imposes constraints on the maximum allowable unauthorized users' SINRs. The design problem, in this case, is, nevertheless, non-convex. Therefore, instead of finding the optimal solution, we examine two different sub-optimal yet low-complexity approaches to solve the problem, namely: Concave-Convex Procedure (CCP) and Semidefinite Relaxation (SDR). Additionally, robust designs that take into account channel uncertainty are also investigated. Extensive numerical results are shown to demonstrate the feasibility and performance of each design with practical parameters.

Multi-Antenna Aided Secrecy Beamforming Optimization for Wirelessly Powered HetNets

The new paradigm of wirelessly powered two-tier heterogeneous networks (HetNets) is considered in this paper. Specifically, the femtocell base station (FBS) is powered by a power beacon (PB) and transmits confidential information to a legitimate femtocell user (FU) in the presence of a potential eavesdropper (EVE) and a macro base station (MBS). In this scenario, we investigate the secrecy beamforming design under three different levels of FBS-EVE channel state information (CSI), namely, the perfect, imperfect and completely unknown FBS-EVE CSI. Firstly, given the perfect global CSI at the FBS, the PB energy covariance matrix, the FBS information covariance matrix and the time splitting factor are jointly optimized aiming for perfect secrecy rate maximization. Upon assuming the imperfect FBS-EVE CSI, the worst-case and outage-constrained SRM problems corresponding to deterministic and statistical CSI errors are investigated, respectively. Furthermore, considering the more realistic case of unknown FBS-EVE CSI, the artificial noise (AN) aided secrecy beamforming design is studied. Our analysis reveals that for all above cases both the optimal PB energy and FBS information secrecy beamformings are of rank-1. Moreover, for all considered cases of FBS-EVE CSI, the closed-form PB energy beamforming solutions are available when the cross-tier interference constraint is inactive. Numerical simulation results demonstrate the secrecy performance advantages of all proposed secrecy beamforming designs compared to the adopted baseline algorithms.