Outdated Channel State Information Research Articles

Dynamic resource allocation for low earth orbit satellite networks

Low earth orbit satellite networks (LSN) have been widely recognized as a key element in the development of next-generation wireless communication networks, offering extensive coverage and seamless connectivity across multiple domains. To achieve the minimum transmission latency for highly-dynamic LSN, this paper first establishes a low earth orbit (LEO) satellite downlink communication model while considering outdated channel state information and high mobility. Then, through the design of user association and satellite power allocation strategies, a dynamic problem of minimizing transmission latency is formulated and solved using successive convex approximation and alternating optimization methods. Simulation results clearly illustrate the substantial reduction in transmission latency achieved by the proposed algorithm, successfully meeting the quality of service demands in dynamic environments during the entire mobility cycle of the LEO satellite.

Secrecy Analysis of Decode-and-Forward Relay Based Heterogeneous Terrestrial RF - Underwater Acoustic System

This study examines how well a heterogeneous dual-hop terrestrial radio frequency and underwater acoustic downlink system maintain confidentiality. It includes an RF source, an intermediate decode-and-forward relay, an underwater multi-hydrophone destination, and a nearby eavesdropper. Eavesdropper overhears underwater acoustic signal transmitted by the relay. RF and underwater acoustic links undergo Nakagami-m and Rayleigh fading, respectively. Due to the propagation delay of underwater scenario, both destination and eavesdropper links are associated with outdated channel state information which also employs maximal-ratio combining. The closed form expressions of secrecy outage probability, strictly positive secrecy capacity, and intercept probability are derived. Monte-Carlo simulations are obtained to certify the analytical expressions.

A Robust Symbol-Level Precoding Method for Multibeam Satellite Systems

Compared to interference mitigating precoding, interference exploiting symbol-level precoding (SLP) requires less transmit power to guarantee the quality of service in a multibeam satellite system. However, due to the large roundtrip time (RTT), it is impractical to obtain real-time channel state information on the satellite side. The random channel state information (CSI) phase error of outdated CSI could cause serious performance deterioration of SLP. To compensate the CSI phase error, we propose an outdated CSI-based robust SLP (RSLP) method, which optimizes the transmit power under outage probability constraints. The central limit theorem (CLT) and second-order Taylor expansion are used to relax the outage probability constraints into convex ones. In addition, because only outdated CSI-based robust block-level precoding exists, we present two comparative RSLP methods accordingly. Without violating outrage probability constraints, the proposed RSLP method requires much lower transmit power than comparative RSLP and existing robust block-level precoding methods. The complexity of the proposed RSLP method is also lower than that of two comparative RSLP methods.

RIS Selection Scheme for UAV-Based Multi-RIS-Aided Multiuser Downlink Network With Imperfect and Outdated CSI

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.

Ergodic Capacity of Intelligent Omni-Surface-Aided Communication Systems With Phase Quantization Errors and Outdated CSI

In this article, we study the performance of an intelligent omni-surface (IOS)-aided communication system, considering the impacts of actual phase quantization errors and outdated channel state information (CSI). Different from the conventional reflective reconfigurable intelligent surface that can only reflect the incident signals, IOS can offer effective communication service to mobile users via the reflective or the transmissive operation. As for the transmissive scenario, we derive the accurate closed-form ergodic capacity (EC) expressions taking into account the phase quantization errors in both the near- and far-fields. To further obtain practical physical insights, tight upper- and lower-bound expressions for EC are provided. It is found that when the transmit power and the quantization bits are sufficiently large, the EC performance would become saturated due to the existence of outdated CSI. Also, the impacts of the phase quantization errors, the outdated CSI, and channel parameters on the considered system performance are revealed. Furthermore, the correctness of our derived expressions is validated by extensive Monte-Carlo simulation results.

Age of Information for Short-Packet Relay Communications in Cognitive-Radio-Based Internet of Things With Outdated Channel State Information

It is crucial to consider time-critical communications in cognitive-radio-based Internet of Things (CR-IoT), which increasingly rely on the exchange of short-packet information to efficiently monitor and control. Therefore, the age of information (AoI) is exploited as a timeliness metric in this paper to investigate the freshness of received information at the remote server with dual-hop short-packet communications in underlay CR-IoT. Since short-packet communications are sensitive to time delay, the influence of outdated factors in the channel state information (CSI) cannot be ignored, so we develop the system under the condition of outdated CSI with Nakagami-m fading channels. Considering the impact of outdated CSI and packets error, we investigate the performance of dual-hop short-packet communications under the classical automatic repeat-request (ARQ) protocol and truncated ARQ (TARQ) protocol, and obtain the approximated expressions of the average AoI and the average peak AoI (PAoI), respectively. Accordingly, the blocklength and the number of retransmissions can be optimized by Fibonacci and iterative algorithms to minimize the average PAoI under two protocols, respectively. Simulation results substantiate the influence of each parameter on the system performance differently under two different protocols. Moreover, the AoI of the dual-hop is also better than that of the single-hop due to the interference threshold of the primary user.

Link Adaptation for Rate Splitting Systems With Partial CSIT

Rate Splitting (RS) has emerged as a flexible multiple-access scheme for Multi-User Multiple-Input Multiple-Output (MU-MIMO) systems, generalizing more conventional approaches such as linear precoding or Non-Orthogonal Multiple Access (NOMA), and providing additional robustness against imperfect channel state information at the transmitter. Actual achievable rates cannot be pre-calculated when facing imperfect and/or outdated channel state information at the transmitter (CSIT), so practical real-time mechanisms in the form of link adaptation (LA) are required. The available CSIT is used to design the RS precoders, which can accommodate some degree of robustness, whereas the physical layer feedback reports the outcome of the decoding of the codewords, in the form of acknowledgement (ACK) or negative ACK (NACK), providing the additional meta-robustness needed when the statistical model of CSIT errors is not accurate, and/or the precoders cannot fully track the channel dynamics. We characterize the outage rate region of RS and analyze the convergence of the complete LA-RS system under partial CSIT (non-accurate and non-permanent), providing relevant insights for addressing the adaptation of private and common rates, designing robust precoders with throughput as relevant metric, and handling the time-variant CSIT quality.

Multi-sinusoidal waveform shaping for integrated data and energy transfer in aging channels

Integrated data and energy transfer (IDET) is capable of simultaneously delivering on-demand data and energy to low-power Internet of Everything (IoE) devices. We propose a multi-carrier IDET transceiver relying on superposition waveforms consisting of multi-sinusoidal signals for wireless energy transfer (WET) and orthogonal-frequency-division-multiplexing (OFDM) signals for wireless data transfer (WDT). The outdated channel state information (CSI) in aging channels is employed by the transmitter to shape IDET waveforms. With the constraints of transmission power and WDT requirement, the amplitudes and phases of the IDET waveform at the transmitter and the power splitter at the receiver are jointly optimised for maximising the average direct-current (DC) among a limited number of transmission frames with the existence of carrier-frequency-offset (CFO). For the amplitude optimisation, the original non-convex problem can be transformed into a reversed geometric programming problem, then it can be effectively solved with existing tools. As for the phase optimisation, the artificial bee colony (ABC) algorithm is invoked in order to deal with the non-convexity. Iteration between the amplitude optimisation and phase optimisation yields our joint design. Numerical results demonstrate the advantage of our joint design for the IDET waveform shaping with the existence of the CFO and the outdated CSI.

Impact of Fading Correlation and Finite-Blocklength Regimes on Secrecy

This paper examines secrecy performance of a three-point system|where a main channel and a wiretap channel are present|under outdated channel state information (oCSI), correlated Rayleigh fading and finite-blocklength (FBL) regimes. A main channel and a wiretap channel experience oCSI severity, which impacts the system's average secrecy capacity (ASC) and secrecy outage probability (SOP). Compact expressions for the ASC and SOP will be offered. Hypothetical bounds give fresh insight into secrecy performance. Impact of FBL regimes on secrecy performance is also shown.

Secrecy analysis of cooperative NOMA-FDR systems with imperfect CSI and colluding eavesdroppers

This paper analyzes the secrecy attributes of a full-duplex relay (FDR) non-orthogonal multiple access (NOMA) system, where the best relay is chosen from a set of numerous relays by using the partial relay selection (PRS) protocol. This best relay assists the communion from source to two NOMA users under the attendance of many colluding eavesdroppers. We obtain the expressions in closed-form of the exact secure outage probability (SOP) of each user, the approximate SOP, and the exact ergodic secrecy capacity (ESC) of the proposed system, taking into consideration the impacts of outdated channel state information (CSI) at source and imperfect CSI at colluding eavesdroppers. We also give the SOP and ESC of the NOMA-FDR system with PRS protocol for comparison with other benchmark systems such as half-duplex (HD)-NOMA, FD-orthogonal multiple access (OMA) systems with PRS protocol, and NOMA-FDR system with multiple-relay (MR) protocol. The simulation results verify the accuracy of our analysis results. Numerical results demonstrate that the SOP and ESC of the NOMA-FDR system greatly vary according to the number of relays and eavesdroppers, SI cancellation capability, channel estimation error, and the correlation coefficient of outdated channels. Most importantly, the NOMA-FDR system has better secrecy performance than all benchmark relay systems.

On Covert Communication Performance in Multi-relay Systems with Outdated CSI

Communication performance significantly depends on the availability of Channel State Information (CSI). This paper investigates the impact of outdated CSI on achievable covert communication performance in a multiple-relay system under a power control scheme. Considering the feedback delay of all links, we first develop an accurate characterization of outdated CSI. Based on this, we formulate theoretical models to depict the inherent relationship between outdated CSI and fundamental covert performance metrics, namely detection error probability (DEP) and covert rate (CR). Utilizing these models, we delve into optimizing CR subject to the DEP constraint, unveiling the maximal CR performance achievable with outdated CSI. Extensive numerical results illustrate the impact of outdated CSI on covert communication performance, also indicating that an increase in the number of relays can mitigate the negative effects of outdated CSI.

Performance Impact of Channel Aging and Phase Noise on Intelligent Reflecting Surface

This letter aims to clarify the impact of channel aging and phase noise on the performance of intelligent reflecting surface-aided wireless systems. We first model mathematically the outdated channel state information (CSI) due to Doppler shifts and phase noise stemming from hardware impairment. Then, a closed-form expression of achievable spectral efficiency under noisy and aged CSI is theoretically derived. Some typical simulation results to numerically demonstrate the performance impact are illustrated.

Deep Reinforcement Learning Based End-to-End Multiuser Channel Prediction and Beamforming

In this paper, reinforcement learning (RL) based end-to-end channel prediction (CP) and beamforming (BF) algorithms are proposed for multi-user downlink system. Different from the previous methods which either require perfect channel state information (CSI), or estimate outdated CSI and set constraints on pilot sequences, the proposed algorithms have no such premised assumptions or constraints. Firstly, RL is considered in channel prediction and the actor-critic aided CP algorithm is proposed at the base station (BS). With the received pilot signals and partial feedback information, the actor network at BS directly outputs the predicted downlink CSI without channel reciprocity. After obtaining the CSI, BS generates the beamforming matrix using zero-forcing (ZF). Secondly, we further develop a deep RL based two-layer architecture for joint CP and BF design. The first layer predicts the downlink CSI with the similar actor network as in the CP algorithm. Then, by importing the outputs of the first layer as inputs, the second layer is the actor-critic based beamforming layer, which can autonomously learn the beamforming policy with the objective of maximizing the transmission sum rate. Since the learning state and action spaces in the considered CP and BF problems are continuous, we employ the actor-critic method to deal with the continuous outputs. Empirical numerical simulations and the complexity analysis verify that the proposed end-to-end algorithms could always converge to stable states under different channel statistics and scenarios, and can beat the existing traditional and learning based benchmarks, in terms of transmission sum rate.

Adaptive Modulation and Coding for Underwater Acoustic Communications Based on Data-Driven Learning Algorithm

With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency.

Power Allocation and Performance Analysis in Overlay Cognitive Cooperative V2V Communication System With Outdated CSI

In this paper, an active-user cooperative scheme for overlay cognitive radio (OCR) vehicle-to-vehicle (V2V) communication system based on three-dimensional (3D) channel model is proposed. Based on the proposed cooperative scheme, the achievable rate regions of the primary users (PU) and secondary users (SU) with outdated channel state information (CSI) are analyzed. According to the tradeoff between the achievable rates of PU and SU, three power allocation schemes are proposed using outdated CSI. The performance of PU and SU in terms of outage events are analyzed. Based on the analytical framework, the simulation results for achievable rate and outage probabilities are provided. And the impact of power allocation coefficient of PU and SU, outdated CSI, signal-to-noise ratio(SNR) and the codes cross-correlation on the proposed active-user cooperation is analyzed. The simulation results are compared with validated analysis to confirm the theoretical analysis.

Instantaneous channel characteristics and progression factor based collaborative routing

Underwater acoustic sensor networks (UWASN) have enormous applications like investigating oceanographic environment, data gathering, scrutiny, calamity avoidance etc. Cooperative communication becomes mandatory when nodes are distributed far apart especially in the ocean environment. The existing relay selection techniques do not consider the instantaneous channel characteristics while selecting the relay nodes. The relays selected based on the outdated channel state information aggravates or worsens the performance of the rapidly changing or dynamic UWASN. Hence, this paper proposes an instantaneous channel characteristics and progression factor (ICPF) based collaborative routing for underwater acoustic sensor networks. It considers numerous indexes such as propagation delay, residual energy, progression factor, spreading, transmission and absorption loss in the forwarding relay node selection. These indexes are averaged and updated periodically to overcome the difficulties caused by the inconsistency and aggressive nature of underwater channel. The progression factor proposed in this work is a prime metric that facilitates efficient data forwarding. Simulation results show that the proposed technique outperforms the existing schemes in terms of packet delivery ratio (PDR), average end to end delay and energy consumption and is capable of achieving PDR of 90.1% for network comprising of 100 acoustic nodes.

Deep Learning-Based Nonstationary Channel Prediction in Tactical Vehicle-to-Vehicle Communication Environments

In this paper, we focus on the vehicle-to-vehicle dynamic channel in tactical communication environments, which shows time-varying and nonstationary characteristics due to the fast mobility, directional antennas, and harsh terrain. These situations present great challenges for the channel state information (CSI) acquisition. To obtain an accurate CSI and reduce pilot overhead, we propose a CSI predictor based on the long short-term memory (LSTM) network. As an improved recurrent neural network (RNN), LSTM units have an excellent learning result on both long- and short-term inputs by adding the gating mechanism. Using the outdated sampling CSI sequence as input data of LSTM units enables the predictor to extract complex data characteristics and capture the temporal law of the nonstationary channel. Simulation results are demonstrated to verify that the LSTM-based predictor has better performance than conventional algorithms in IEEE 802.11p standard. Additionally, the key factors that affect the performance of the proposed predictor are further analyzed.

Deep Reinforcement Learning-Based Adaptive Modulation for Underwater Acoustic Communication with Outdated Channel State Information

Underwater acoustic (UWA) adaptive modulation (AM) requires feedback about channel state information (CSI) but the long propagation delays and time-varying features of UWA channels can cause the CSI feedback to be outdated. When the AM mode is selected by outdated CSI, the mismatch between the outdated CSI and the actual CSI during transmission degrades the performance and can even lead to communication failure. Reinforcement learning has the ability to learn the relationships between adaptive systems and the environment. This paper proposes a deep Q-network (DQN)-based AM method for UWA communication that uses a series of outdated CSI as the system input. Our study showed that it could extract channel information and select appropriate modulation modes in the expected channels more effectively than single Q-learning (QL) without needing a deep neural network structure. Furthermore, to mitigate any decision bias that was caused by partial observations of UWA channels, we improved the DQN-based AM by integrating a long short-term memory (LSTM) neural network, named LSTM-DQN-AM. The proposed scheme could enhance the DQN’s ability to remember and process historical input channel information, thus strengthening its relationship mapping ability for state-action pairs and rewards. The pool and sea experimental results demonstrated that the proposed LSTM-DQN-AM outperformed DQN-, QL- and threshold-based AM methods.

Outdated Access Point Selection for Mobile Edge Computing With Cochannel Interference

In this paper, we investigate a mobile edge computing (MEC) network, where the user has some computational tasks to be assisted by multiple computational access points (CAPs) through offloading. We consider practical communication scenarios with limited spectrum resources, and the cochannel interference arising from the aggressive reuse of frequency severely degrades the system offloading performance. To enhance the system performance, we provide three CAP selection criteria to choose one best CAP among multiple ones. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">criterion I</i> maximizes the computational capability at the CAP, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">criterion II</i> minimizes the interfering power, while <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">criterion III</i> maximizes the instantaneous channel gain of data link. In time-varying channel environments, the CAP selection may be outdated, which deteriorates the system performance. For the three criteria, we evaluate the system outage probability in the outdated channel state information (CSI) by taking into account the latency, energy consumption and data rate, and provide the analytical and asymptotic expressions of outage probability, from which we obtain some critical insights on the system design. Simulation results are finally demonstrated to verify the proposed studies. In particular, criterion III under the perfect CSI can achieve the system whole diversity order coming from multiple CAPs.

Secrecy Performance of Underlay Cognitive Radio Networks With Primary Interference

This work explores the physical layer security of an underlay cognitive radio network (CRN) in the presence of a powerful interferer called a primary transmitter (PT) under peak interference power constraint. Under this scenario, the secondary transmitter (Alice) transmits secret information on a single antenna with the highest signal-to-interference plus noise ratio (SINR) to a legitimate receiver (Bob), and an eavesdropper (Eve) attempts to intercept that information. Depending upon the availability of channel state information (CSI) of the eavesdropper channel, we investigate the secrecy performance of the proposed network in the Rayleigh fading environment for both passive and active eavesdropping scenarios. We obtain analytical expressions for the secrecy outage probability, intercept probability, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -outage secrecy capacity in a passive eavesdropping scenario. A complete investigation of the average secrecy capacity (ASC) has been made, and novel expressions for the exact and asymptotic ASC have been obtained for active eavesdropping. Bolstering these results, we also define a high SINR power offset, which quantifies the significance of main system parameters on the ASC explicitly. Further, the effect of outdated CSI of Alice to primary receiver link on the various performance metrics with the concept of interference-outage is also studied.