Current Channel State Information Research Articles

Performance evaluation of pre-equalized UVLC links over outdated lognormal turbulence channels

Underwater visible light communication (UVLC) is important for various underwater applications, including diver-to-diver information sharing, oil field exploration, port security, underwater surveillance systems, and environmental monitoring. However, it should be remembered that UVLC links are strongly affected by underwater optical turbulence (UOT). This may necessitate frequent adjustments in transmit power based on current channel state information (CSI) to mitigate fading effects. In some applications, such as diver-to-diver links, the quasi-static variations in the channel coefficient between transmission frames—attributable to the semi-fixed positions of the transmitting and/or receiving nodes—lead to practical implementations of the transmit power selection that may rely on outdated CSI. In this paper, we investigate the degradation in error rate performance caused by the use of outdated channel information in setting transmit power. Especially, we derive a closed-form expression for the bit error rate (BER) for a pre-equalized UVLC link over outdated lognormal turbulence channels. We verify the derived expression using Monte Carlo simulations.

Adaptive-Duplex Relay Selection for Cooperative Non-Orthogonal Multiple Access Networks

To further improve the spectral efficiency of the cooperative non-orthogonal multiple access (NOMA) network with half-duplex (HD) relaying and avoid the zero diversity gain due to the imperfect self interference estimation at high signa-to-noise ratio (SNR) value with full-duplex (FD) relaying, this paper developed a novel two-stage relay selection for a two-user multi-relay cooperative NOMA systems with adaptive-duplex (AD) relaying which can switch opportunistically between HD and FD relaying modes. An adaptive duplex based on fixed power allocation factor relay selection (AD-FPA-RS) scheme for cooperative NOMA networks is proposed in this paper, the relay can switch the work mode to the FD or HD state adaptively according to the current channel state information and the result of decode at the relay. Futhermore, the exact outage probability for the proposed AD-FPA-RS scheme is obtained in closed-form expression. The simulation and numerical results show that the proposed AD-FPA-RS scheme not only outperforms the existing HD-FPARS and FD-FPA-RS schemes in terms of outage behavior significantly, but also can obtain full diversity gain without the floor outage performance at high SNR region.

Adaptive-Duplex Relay Selection for Cooperative Non-Orthogonal Multiple Access Networks

To further improve the spectral efficiency of the cooperative non-orthogonalmultiple access (NOMA) network with half-duplex (HD) relaying and avoid the zero diversity gain due to the imperfect self interference estimation at high signa-to-noise ratio (SNR) value with full-duplex (FD) relaying, this paper developed a novel two-stage relay selection for a two-user multi-relay cooperative NOMA systems with adaptive-duplex (AD) relaying which can switch opportunistically between HD and FD relaying modes. An adaptive duplexbased on a fixed power allocation factor relay selection (AD-FPA-RS) scheme for cooperative NOMA networks is proposed in this paper, the relay can switch the work mode to the FD or HD state adaptively according to the current channel state information and the result of decode at the relay. Furthermore, the exact outage probability for the proposed AD-FPA-RS scheme is obtained in a closed-form expression. The simulation and numerical results show that the proposed AD-FPA-RS scheme not only outperforms the existing HD-FPARS and FD-FPA-RS schemes in terms of outage behavior significantly but also can obtain full diversity gain without the floor outage performance at high SNR region.

Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services

Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users.

A time-varying acoustic channel-aware topology control mechanism for cooperative underwater sonar detection network

Topology control plays a vital role in cooperative underwater sonar detection networks (USDNs) to ensure detection applications. By incorporating spatio-temporal uncertainties in acoustic channel and limited resources in underwater networks, a large number of node deployment schemes have been proposed to overcome these difficulties through topology control. However, the channel uncertainty has not been solved fundamentally. In order to effectively deal with the time-space-frequency uncertainty of the acoustic channel, this paper proposes a time-varying acoustic channel-aware (TVAC) topology control mechanism to deploy sonar nodes in USDNs. Initially, a time-varying acoustic channel state information (CSI) estimation algorithm and a prediction algorithm are proposed by applying three sub-modules: a feature stitching deep neural network-based temperature prediction model that we designed, a deep neural network-based sound velocity estimation model, and a BELLHOP-based propagation loss calculation model. After that, a TVAC topology control mechanism is adopted to adaptively deploy nodes based on the CSI estimation and prediction results. Using the improved weighted set covering algorithm, the current CSI is adopted to determine the working modes of sonar nodes, and the predicted CSI is adopted to obtain the cycle of topology update. What is more, we develop an offline–online CSI prediction model to improve the accuracy and stability of node working modes management. Simulation results show that compared with other topology control schemes, TVAC can obtain superior coverage performance, full connectivity, and prolong the network lifetime with guaranteed coverage and connectivity.

Dynamic Offloading Strategy for Delay-Sensitive Task in Mobile-Edge Computing Networks

Mobile-edge computing (MEC) technology offers computing resources for mobile devices to conduct computationally heavy activities by putting servers at the wireless mobile network’s edge. This mitigates the scarcity of computing resources in mobile devices and enhances the intelligence of the Internet of Things (IoT), which is a crucial technology for achieving industrial digitalization. Considering the time-varying channel as well as the time-varying available computing resources of MEC servers, this article formulates a hybrid optimization problem that combines task offload and resource allocation. The goal is to minimize MEC servers’ overall power consumption. Since the channel state information (CSI) stored in the MEC system is not real time, we propose a reinforcement learning (RL) algorithm for predicting current CSI from historical CSI and obtain the optimal strategy for task offloading. On the other hand, convex optimization methods are used to accomplish the dynamic resource allocation strategy. In addition, an approach based on deep RL (DRL) is put forward to overcome the dimensionality curse in RL algorithms. The simulation experiments illustrate that the proposed algorithms outperform the nonpredictive schemes by a large margin, and their performance is close to that of the optimum scheme, which utilizes simultaneous CSI.

Online Multicell Coordinated MIMO Wireless Network Virtualization With Imperfect CSI

We consider online coordinated precoding design for downlink wireless network virtualization (WNV) in a multi-cell multiple-input multiple-output (MIMO) network with imperfect channel state information (CSI). In our WNV framework, an infrastructure provider (InP) owns each base station that is shared by several service providers (SPs) oblivious of each other. The SPs design their precoders as virtualization demands for user services, while the InP designs the actual precoding solution to meet the service demands from the SPs. Our aim is to minimize the long-term time-averaged expected precoding deviation over MIMO fading channels, subject to both per-cell long-term and short-term transmit power limits. We propose an online coordinated precoding algorithm for virtualization, which provides a fully distributed semi-closed-form precoding solution at each cell, based only on the current imperfect CSI without any CSI exchange across cells. Taking into account the two-fold impact of imperfect CSI on both the InP and the SPs, we show that our proposed algorithm is within an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\delta)$ </tex-math></inline-formula> gap from the optimum over any time horizon, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> is a CSI inaccuracy indicator. Simulation results validate the performance of our proposed algorithm under two commonly used precoding techniques in a typical urban micro-cell network environment.

Unconditional Authentication Based on Physical Layer Offered Chain Key in Wireless Communication.

Authentication is a critical issue in wireless communication due to the impersonation and substitution attacks from the vulnerable air interface launched by the malicious node. There are currently two kinds of authentication research in wireless communication. One is based on cryptography and relies on computational complexity, the other is based on physical layer fingerprint and can not protect data integrity well. Both of these approaches will become insecure when facing attackers with infinite computing power. In this paper, we develop a wireless unconditional authentication framework based on one-time keys generated from wireless channel. The proposed unconditional authentication framework provides a new perspective to resist infinite computing power attackers. We study the performance of the unconditional authentication framework in this paper. First, a physical layer offered chain key (PHYLOCK) structure is proposed, which can provide one-time keys for unconditional authentication. The physical layer offered chain keys are generated by XORing the physical layer updated keys extracted from the current channel state information (CSI) and the previous chain keys. The security of PHYLOCK is analyzed from the perspective of information theory. Then, the boundary of the deception probability is conducted. It is shown that unconditional authentication can achieve a probability of deception , where is the entropy of the one-time key used for one message. Finally, the conditions for unconditional authentication are listed. Our analysis shows that the length of the key and the authentication code need to be twice the length of the message and the encoding rules of the authentication code need to satisfy the restrictions we listed.

Deep Reinforcement Learning Based Adaptive Modulation With Outdated CSI

The problem of adaptive modulation with outdated channel state information (CSI) is considered. Best existing approach to tackle this problem relies on using a (non-)linear auto-regressive moving average (ARMA) model to predict current CSI from outdated values. This approach is valid only if the wireless channel variations over time behave in a linear or smooth enough nonlinear fashion, which is not necessarily the case. We propose a deep reinforcement learning based adaptive modulation (DRL-AM) approach that can handle this limitation. While DRL-AM is more complex than (non-)linear AR(MA), it performs significantly better as corroborated via numerical results on real channel measurements. Furthermore, compared to capacity-achieving codes, complexity is moved from receiver to transmitter making this approach suitable for receiving nodes with limited resources such as internet of things (IoT) devices.

Joint user clustering and salp based particle swarm optimization algorithm for power allocation in MIMO-NOMA

Non-Orthogonal Multiple Access (NOMA) provides a positive solution for multiple access issues and meets the criteria of fifth-generation (5G) networks by improving service quality that includes vast convergence and energy efficiency. The problem is formulated for maximizing the sum rate of MIMO-NOMA by assigning power to multiple layers of users. In order to overcome these problems, two distinct evolutionary algorithms are applied. In particular, the recently implemented Salp Swarm Algorithm (SSA) and the prominent Optimization of Particle Swarm (PSO) are utilized in this process. The MIMO-NOMA model optimizes the power allocation by layered transmission using the proposed Joint User Clustering and Salp Particle Swarm Optimization (PPSO) power allocation algorithm. Also, the closed-form expression is extracted from the current Channel State Information (CSI) on the transmitter side for the achievable sum rate. The efficiency of the proposed optimal power allocation algorithm is evaluated by the spectral efficiency, achievable rate, and energy efficiency of 120.8134bits/s/Hz, 98Mbps, and 22.35bits/Joule/Hz respectively. Numerical results have shown that the proposed PSO algorithm has improved performance than the state of art techniques in optimization. The outcomes on the numeric values indicate that the proposed PSO algorithm is capable of accurately improving the initial random solutions and converging to the optimum.

Energy Optimization For Hybrid ARQ With Turbo Coding: Rate Adaptation and Allocation

We consider incremental redundancy (IR) hybrid automatic repeat request (HARQ) over independent block-fading channels with turbo coding. We consider different cases of channel state information (CSI) at the transmitter: the transmitter has no knowledge of any CSI, or knows the CSI in previous transmission rounds through a perfect feedback channel, or knows both current and previous CSI. The transmitter decides the forward error correction code rate based on the CSI it has. We minimize the energy consumption of turbo-coded HARQ, subject to a packet loss rate constraint. Numerical results show that the energy consumption of HARQ decreases when more CSI information is available at the transmitter. We also compare IR combining with Chase combining and the system without combining, and IR combining yields the least energy consumption.

CSI learning based active secure coding scheme for detectable wiretap channel

In this study, the authors consider the problem of secure and reliable communication with uncertain channel state information (CSI) and present a new solution named active secure coding which combines the machine-learning methods with the traditional physical-layer secure coding scheme. First, the authors build a detectable wiretap channel model by combining the hidden Markov model with the compound wiretap channel model, in which the varying of channel block CSI is a Markov process and the detected information is a stochastic emission from the current CSI. Next, the authors present a CSI-learning scheme to learn the CSI from the detected information by the Baum–Welch and Viterbi algorithms. Then the authors construct explicit secure polar codes based on the learned CSI, and combine it with the CSI-learning scheme to form the active secure polar coding scheme. Simulation results show that an acceptable level of reliability and security can be achieved by the proposed active secure polar coding scheme.

Joint Long-Term Admission Control and Beamforming in Green Downlink Networks: Offline and Online Approaches

Admission control is an effective solution to managing a wireless network with a large user set. It dynamically rejects users in bad conditions, and thereby guarantees high quality-of-service (QoS) for the others. Unfortunately, a frequently-varying admissible user set requests remarkable power to continually re-establish the transmission link. Hence, we explore the stability of admissible user set, and formulate a joint long-term admission control and beamforming problem for a network with one multi-antenna base station (BS) and multiple single-antenna users. We consider the downlink transmission in a time period containing multiple time slices. By jointly optimizing the admissible users and the BS transmit beamformers in different time slices, we aim to minimize the total power cost, including not only the power for QoS guarantee, but also the power for user status switching. Due to the NP-hardness of the problem, we develop two (offline and online) algorithms to find some efficient suboptimal solutions. The offline algorithm requires all channel state information (CSI), and solves the admissible users and beamformers in different time slices in one shot, based on successive upper-bound minimization (SUM). To support real-time data transmission, we further design an alternating direction method of multipliers (ADMM)-based online algorithm, which outputs the admissible users and beamformers in different time slices successively, utilizing the previous admissible user set, the actual value of current CSI, and the distribution of future CSI. Simulations validate the performance of the two algorithms, and show that the online algorithm is an efficient practical alternative to the offline algorithm.

Real-Time Resource Allocation for Wireless Powered Multiuser Mobile Edge Computing With Energy and Task Causality

This article considers a wireless powered multiuser mobile edge computing (MEC) system, in which a multi-antenna hybrid access point (AP) wirelessly charges multiple users, and each user relies on the harvested energy to execute computation tasks. We jointly optimize the energy beamforming and remote task execution at the AP, as well as the local computing and task offloading, aiming to minimize the total system energy consumption over a finite time horizon, subject to causality constraints for both energy harvesting and task arrival at the users. In particular, we consider a practical scenario with casual task state information (TSI) and channel state information (CSI), i.e., only the current and previous TSI and CSI are available, but the future TSI and CSI can only be predicted subject to certain errors. To solve this real-time resource allocation problem, we propose an offline-optimization inspired online design approach. First, we consider the offline optimization case by assuming that the TSI and CSI are perfectly known a-priori . In this case, the energy minimization problem corresponds to a convex problem, for which the semi-closed-form optimal solution is obtained via the Lagrange duality method. Next, inspired by the optimal offline solution, we propose a sliding-window based online resource allocation design in practical cases by integrating with the sequential optimization. Finally, numerical results show that the proposed joint wireless powered MEC designs significantly improve the system’s energy efficiency, as compared with the benchmark schemes that consider a sliding window of size one or without such joint optimization.

Optimal multi-part mobile computation offloading with hard deadline constraints

This paper considers mobile computation offloading when concurrent local execution (CLE) is used to enforce task execution time constraints. This mechanism can ensure that hard task deadlines are satisfied regardless of any randomness induced by the wireless channel, network or cloud servers. In this type of system, mobile device energy may be reduced by segmenting a task upload into multiple parts, rather than doing a conventional contiguous task upload. The paper uses this mechanism to adapt to changes in channel conditions during the offload. Unlike the contiguous task offload case however, the upload initiation times of each part must be determined dynamically. This is done while ensuring that hard task deadlines are always satisfied. In this paper, the multi-part computation offloading case is considered. In multi-part offloading, the task to be offloaded is partitioned into K upload parts before any offload initiation decisions are made. In this case, current channel state information is incorporated into the offload decisions, and the system must always satisfy a hard task execution time constraint using concurrent local execution. The paper considers the case for Markovian wireless channels. A provably energy-optimal online computation offloading algorithm (MuliOpt) is introduced for multi-part offloading. MultiOpt is shown to be optimal using Markovian decision process stopping theory. Since the computational complexity of MultiOpt can be significant, simpler and more computationally efficient heuristics, which also respect the hard task execution deadline, may be used. The paper introduces two such heuristics, the Immediate Offloading, and Multi Threshold algorithms. The mobile energy use of MultiOpt is compared to these heuristics, as well as to local execution without offloading and an offline energy bound. Simulation results show that MultiOpt performs significantly better when compared to the proposed heuristics, as well as when K increases.

Deep learning based physical layer security of D2D underlay cellular network

In order to improve the physical layer security of the device-to-device (D2D) cellular network, we propose a collaborative scheme for the transmit antenna selection and the optimal D2D pair establishment based on deep learning. Due to the mobility of users, using the current channel state information to select a transmit antenna or establish a D2D pair for the next time slot cannot ensure secure communication. Therefore, in this paper, we utilize the Echo State Network (ESN) to select the transmit antenna and the Long Short-Term Memory (LSTM) to establish the D2D pair. The simulation results show that the LSTM-based and ESN-based collaboration scheme can effectively improve the security capacity of the cellular network with D2D and increase the life of the base station.

Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation

In mobile communications systems, fast link adaptation (FLA) aims to achieve high system throughput of each user by accurately determining a channel quality indicator (CQI) for feedback, which predicts the next channel based on the current channel state information. In this paper, we propose an improved calibration method of mutual information effective signal-to noise-ratio mapping (MIESM) to determine an accurate CQI feedback value in FLA. Our proposed calibration method derives the optimal calibration factors by considering various channel environments and setting the effective interval of effective signal-to-noise ratio, which is a single value compressing the information of channel characteristics at a time. The simulation is performed in various signal-to-noise ratio (SNR) ranges to account for the actual environments, and the calibration factors are derived from the proposed calibration method. The results show that the CQI feedback value from the derived calibration factors are more accurate than the existing calibration factors. In addition, we discuss a study regarding the time-coherence-based CQI feedback bit reduction scheme. Assuming that each channel is correlated to the previous and subsequent channels, we propose a method to reduce the number of CQI feedback bits adapted to the corresponding SNR regime. Through the simulation, we compare the system throughputs of the proposed adaptive CQI feedback and the conventional CQI feedback scheme. As a result, the proposed CQI feedback has almost the same system throughput as the conventional CQI feedback scheme, but the average number of feedback bits is reduced, thereby improving the efficiency of the communication.

Cognitive Beamforming in Radar Bands

We investigate the problem of beamformer design for communications systems coexisting with legacy radar systems with mechanically rotating antennas. Based on measurement campaign findings that support the assumption of fast decoherence for the radar-to-base station interference within a radar rotation, as well as the assumption of very strong correlation of interference channel realizations corresponding to the same radar rotation phase, we introduce beamforming solutions exploring these channel characteristics, without imposing the need for current channel state information. Different beamformer designs are proposed, characterized by different levels of computational complexity, that are applicable both in the case of synchronous systems, where the communication system is aware of the radar rotation phase, as well as in the case of asynchronous systems.

Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting

For the problem of channel state information (CSI) delay and error, this paper proposes a joint interference and phase alignment algorithm based on Bayesian estimation and power allocation among data streams for multicell, multiple-input multiple-output broadcast channels (MIMO-BC). Firstly, the sender obtains the best estimate of the current CSI through Bayesian estimation. Secondly, the interference suppression matrix is designed by maximizing the ratio of the desired signal power to the intercell interference plus noise ratio (SINR) in the forward link, and in the reverse communication, by maximizing the SINR design precoding. Further, the water-filling algorithm is combined to optimize power allocation among data streams. Finally, the phase alignment is used to rotate the interference between data streams into the signal space of the target receive data stream, thereby enhancing the received power of the target data stream. Simulation results show that the proposed algorithm has certain performance advantages over other algorithms, whether it is ideal CSI or delay and error CSI.

Stable Beamforming With Low Overhead for C/U-Plane Decoupled HSR Wireless Networks

Millimeter wave (mmWave) directional communications have become a vital 5G technique to achieve the goal of multiple gigabit data rate with low latency, which also hold true for high-speed railway (HSR) wireless networks. However, the high mobility in HSR scenarios causes frequent beam realignments and consequently high feedback overheads, degrading the transmission reliability, and efficiency. In mmWave beamforming systems, besides transmitters (TXs) and receivers (RXs) are also equipped with antenna arrays, which equivalently introduces a new degree of freedom for link adaptation. Based on this observation, a stable beamforming scheme with low overhead, consisting of both long-term TX beamforming and short-term RX beamforming, is proposed. In the long-term TX beamforming, under the premise of satisfying the basic receiver sensitivity, TXs are devised to use as wide beams as possible to extend the serving time of each beam. In the short-term RX beamforming, within a TX beam, RXs autonomously adjust RX beams according to the current channel state information instead of frequently feeding it back to TXs, thereby reducing feedback overheads and accelerating link adaptation. Through this scheme, the received signal to noise ratio is kept at a high and stable level. To meet the strict transmission reliability requirements in HSRs, the control/user-plane decoupled architecture is applied to mitigate the inherent blindness of mmWave directional beams. Theoretical and numerical results show that the proposed scheme can significantly reduce feedback overheads and guarantee the desired performance.