Unknown Channel State 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.

Causal Learning for Robust Specific Emitter Identification Over Unknown Channel Statistics

Causal Learning for Robust Specific Emitter Identification Over Unknown Channel Statistics

Intelligent Reflecting Surface Aided Target Localization With Unknown Transceiver-IRS Channel State Information

Intelligent Reflecting Surface Aided Target Localization With Unknown Transceiver-IRS Channel State Information

Disentangled Representation Learning for RF Fingerprint Extraction Under Unknown Channel Statistics

Deep learning (DL) applied to a device’s radio-frequency fingerprint (RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation (MLE). Although their discriminability has recently been extended to unknown devices in open-set scenarios, they still tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation (DR) learning that first learns to factor the signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it shuffles these two parts within a dataset for implicit data augmentation, which imposes a strong regularization on RFF extractor learning to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-based RFF, outperforms conventional methods in terms of generalizability to unknown devices under unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight (LoS) propagation paths.

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.

Multisensor Scheduling for Remote State Estimation Over a Temporally Correlated Channel

This article studies multisensor scheduling for remote state estimation in cyber-physical systems. We consider that each sensor monitors a dynamic process and sends its data to the remote end. This article focuses on minimizing remote estimation errors over a temporally correlated communication channel. The problem is formulated as the Markov decision process (MDP) with finite-horizon cost criterion. The optimal structured policies are derived for both Markov packet dropout and finite-state Markov channel models, which can reduce computation overhead. For the infinite-horizon case, we design algorithms to address the issues of unknown channel statistics and the curse of dimensionality in the MDP, respectively. Particularly, a heuristic algorithm with linear complexity is proposed to schedule multisensor in a decentralized manner. Simulation examples are provided to verify the theoretical results.

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.

Transmission schedule for jointly optimizing remote state estimation and wireless sensor network lifetime

This paper studies scheduling multiple sensors for jointly optimizing remote state estimation and wireless sensor network lifetime. Each sensor in the network sends its packet to the remote center for further estimation over wireless communication channels. Sensors in the network will inevitably run out of battery power due to transmission consumption and then reach the end of sensor network lifetime. This article focuses on the problem how to schedule multiple sensors minimizing the total remote estimation errors and simultaneously prolonging the sensor network lifetime. The problem is formulated as a special class of Markov decision process (MDP), stochastic shortest path (SSP) problem. Under this framework, we first derive a set of conditions to guarantee the optimality of structural policies in SSP. Subsequently, threshold structure of the optimal scheduling scheme is obtained by verifying the conditions. Moreover, two Value-of-Information-based multi-sensor scheduling algorithms and a linear-architecture-based learning algorithm are designed to tackle the curse of dimensionality and unknown channel statistics in large-scale sensor networks. Numerical simulations are given to validate effectiveness of our results.

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

Energy efficient management for distributed state estimation under DoS attacks

AbstractIn this article, we consider the issue of energy efficient management for distributed state estimation over unknown packet‐dropping communication channels which are exposed to Denial‐of‐Service (DoS) attacks. After a packet is transmitted, the sensor determines whether to retransmit and the retransmission power levels based on the feedback information from the receiving sensor. We formulate the energy efficient management problem as a modified Markov decision problem (MMDP), and proposed an algorithm to seek a retransmission power schedule that achieves optimal tradeoff between energy consumption and communication rate for given channel noise power. In the case of unknown channel state, we design a statistical learning method to estimate channel noise power. Besides, inspired by the hypothesis testing, we design an anomaly detector to guarantee the instantaneity of channel noise power estimation and efficiency of energy management. Furthermore, we derive the optimal gain for each estimator, and provide a sufficient condition for the stability of estimator under the energy management. Finally, we verify the main results by numerical examples.

Exploring UAV's multi-domain joint anti-jamming intelligent decision algorithm

To understand the complex communication environment of a UAV in battlefield, its unknown channel statistics information and poor intelligent jamming and anti-jamming capability, the multi-domain anti-jamming problem is studied, and a multi-domain joint anti-jamming intelligent decision algorithm is proposed. First, the channel selection method is adopted to deal with jamming in the frequency domain. A multi-arm slot machine's channel selection model is established, and the channel interference level is judged. Secondly, the moderate interference channel is suppressed in the power domain, and the model of its Stackelberg game is established. The game equalization is solved to obtain the best transmission power and reduce the overhead caused by channel switching. The simulation results show that the long-term rewards of the intelligent decision algorithm are significantly higher than those of the traditional multi-arm slot machine's algorithm and the random selection algorithm and that the average throughput of the communication system of the UAV is improved, thus proving the superiority of the intelligent decision algorithm.

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.

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.

Learning Optimal Scheduling Policy for Remote State Estimation Under Uncertain Channel Condition

We consider optimal sensor scheduling with unknown communication channel statistics. We formulate two types of scheduling problems with the communication rate being a soft or hard constraint, respectively. We first present some structural results on the optimal scheduling policy using dynamic programming and assuming that the channel statistics is known. We prove that the $Q$ -factor is monotonic and submodular, which leads to thresholdlike structures in both problems. Then, we develop a stochastic approximation and parameter learning frameworks to deal with the two scheduling problems with unknown channel statistics. We utilize their structures to design specialized learning algorithms. We, then prove the convergence of these algorithms. Performance improvement compared with the standard $Q$ -learning algorithm is shown through numerical examples, which will also discuss an alternative method based on recursive estimation of the channel quality.

A QoE-Oriented Network Selection Mechanism in Heterogeneous Wireless Networks

In order to guarantee the UAV video uplink in heterogeneous networks, a novel access network selection mechanism was proposed in this paper. Due to the unknown Channel State Information(CSI), the issue was first modeled as the Multi-Armed Bandit (MAB) problem. Then, the reward function was built and the Quality of Experience (QoE) of video was considered. By means of three typical solving algorithms, decisions could be made between utilization and exploration. The simulation results show that the mechanism can effectively solve the network selection problem of video transmission in heterogeneous networks.

Fast Learning for Dynamic Resource Allocation in AI-Enabled Radio Networks

Artificial Intelligence (AI)-enabled radios are expected to enhance the spectral efficiency of 5th generation (5G) millimeter wave (mmWave) networks by learning to optimize network resources. However, allocating resources over the mmWave band is extremely challenging due to rapidly-varying channel conditions. We consider several resource allocation problems for mmWave radio networks under unknown channel statistics and without any channel state information (CSI) feedback: i) dynamic rate selection for an energy harvesting transmitter, ii) dynamic power allocation for heterogeneous applications, and iii) distributed resource allocation in a multi-user network. All of these problems exhibit structured payoffs which are unimodal functions over partially ordered arms (transmission parameters) as well as over partially ordered contexts (side-information). Unimodality over arms helps in reducing the number of arms to be explored, while unimodality over contexts helps in using past information from nearby contexts to make better selections. We model this as a structured reinforcement learning problem, called contextual unimodal multi-armed bandit (MAB), and propose an online learning algorithm that exploits unimodality to optimize the resource allocation over time, and prove that it achieves logarithmic in time regret. Our algorithm’s regret scales sublinearly both in the number of arms and contexts for a wide range of scenarios. We also show via simulations that our algorithm significantly improves the performance in the aforementioned resource allocation problems.

BER Performance Investigation of WPM with Unknown CSI Using Time-Domain MMSE Equalization

Wavelet Packet Modulation (WPM) is an emerging Multi-Carrier Modulation (MCM) technique regarded as a potential alternative to widely used Orthogonal Frequency Division multiplexing (OFDM). OFDM uses Cyclic Prex (CP) and can rely on one-tap equalization. Since WPM has overlapping symbols in the time domain, equalization cannot depend on the use of CP. This paper considers time-domain Minimum Mean-Square Error (MMSE) equalization for WPM with unknown Channel State Information (CSI), where equalizer filter coefficients are computed using known training sequences. The infuence of the transmission overhead due to training sequences on the Bit Error Rate (BER) performance is investigated considering quasistatic time-varying Rayleigh fading channels. Numerical results from computer simulations show that, with moderate overhead due to training sequences, BER performances are comparable with the case of known CSI. Furthermore, WPM is compared to OFDM with one-tap equalization, and is shown to provide better BER performances as compared to OFDM. Finally, this paper demonstrates improvement in Peak-to-Average Power Ratio (PAPR) of the transmitted signal using subcarrier combining for WPM in heterogeneous communication environments, where low-rate and high-rate devices share the same transmission resources.