Imperfect Channel State Information Scenario Research Articles

A soft actor–critic reinforcement learning approach for over the air active beamforming with reconfigurable intelligent surface

Combining reconfigurable intelligent surface (RIS) and unmanned aerial vehicle (UAV) provides ubiquitous connectivity for 6G air–ground communications, effectively enhancing coverage. However, due to the ”multiplicative fading” effect, passive RIS can only offer weak capacity gain. In addition, the mobility of UAVs may lead to imperfect channel state information (CSI), making it difficult to perform accurate beamforming. To address these issues, this paper adopts active RIS to actively amplify the reflected signals to overcome the high path loss caused by ”multiplicative fading”. To adapt to the randomness of channel changes, this paper employs the soft actor–critic (SAC) algorithm, which is based on the maximum entropy strategy. This approach jointly optimizes the precoding of the base station (BS) and the beamforming of the aerial RIS (ARIS), aiming to maximize the multi-user transmission rate. Simulation results show that when active ARIS is employed, the proposed algorithm achieves similar sum-rate results in imperfect CSI and perfect CSI scenarios and realizes 71% and 74% performance improvement compared to the traditional passive RIS, respectively. Moreover, the sum-rate remains stable within a certain range when the UAV hovers at any position between the BS and the user.

Power Allocation for Adaptive-Connectivity Wireless Networks Under Imperfect CSI

In this paper, we propose a power allocation algorithm to guarantee quality of experience (QoE) while considering the impact of imperfect channel state information (CSI) in multi-connectivity multiple-input multiple-output (MIMO) systems. Multi-connectivity is a promising solution for wireless technologies to fulfill ever-increasing demands for QoE via massive MIMO. However, the performance of MIMO systems mostly depends on the accuracy of the channel estimation algorithm that provides CSI. Under imperfect CSI scenarios, increasing transmit power to satisfy QoE for one user exacerbates interference with others, thus leading to a tradeoff between power consumption and user satisfaction. To guarantee QoE while minimizing transmission power under imperfect CSI, the effect of imperfect CSI is quantified, and a closed form signal-to-interference-plus-noise ratio (SINR) is derived. Then, a two-stage algorithm categorizes UEs into two groups: those that only need single connectivity and those that need dual connectivity (i.e., adaptive connectivity). After classification, transmission power is allocated to guarantee QoE via a power control strategy that minimizes the transmission power. By comparing performance with a single connectivity–based algorithm and fixed multi-connectivity–based algorithms, we show that our proposed algorithm not only satisfies all the UEs in the system but also consumes less transmission power than the benchmarks.

Fluid Antenna Enabling Secret Communications

Recent researches have revealed that fluid antenna, a new position-switchable antenna technology, can make use of the spatial moments of deep fades of the interference signal occurred naturally due to multipath for multiple access. In this letter, this phenomenon is exploited in a physical layer security setup where a base station (BS) transmits an information-bearing signal to a legitimate user and an artificial noise (AN) signal to a potential eavesdropper while the user is equipped with a fluid antenna to overcome the AN signal. The proposed approach needs no effort from the base station (BS) to avoid the AN signal from harming the legitimate user. Trying to enhance the secrecy rate, we study the power allocation of the information-bearing and AN signals if the channel state information (CSI) is available at the BS. Both perfect and imperfect CSI scenarios are investigated. Simulation results demonstrate that the proposed system, with a single fluid antenna with one radio-frequency (RF) chain at the user, achieves the secrecy rate that is achievable by the user utilizing maximal ratio combining (MRC) with many antennas instead, and is the only approach robust to CSI uncertainties of the eavesdropper, requiring no power allocation nor beamforming at the BS.

Full-Duplex MIMO Relay-Assisted Interference Alignment Algorithm in K-user Interference Channels

In this paper, we investigate the transceiver design schemes for the full-duplex multiple-input multiple-output relay-assisted K-user single-input multiple-output interference channels. Firstly, we propose an iterative optimized reference vector for IA (IORV-IA) algorithm in the perfect channel state information (CSI) scenario. The proposed IORV-IA algorithm not only achieves the alignment of interference signals at each receiver, but also iteratively optimizes the IA reference vector by orthogonalizing the directions of the interference signals and the desired signal. With the optimized IA reference vector, the relay processing matrix and the receiving filter vectors are designed to further improve the system performance. Considering that the relay cannot obtain perfect CSIs in practice due to many factors, and the performance of the IA scheme is very sensitive to this error. Furthermore, we propose a robust transceiver design scheme based on mean square error (MSE) in the imperfect CSI scenario, which minimizes the sum of MSEs in the worst case through iteration. The proposed algorithms are evaluated in terms of the average sum rate and bit error rate performance and the simulation results show the advantages of the proposed algorithms over existing centralized IA and centralized zero-forcing algorithms.

Q-Learning-Aided Offloading Strategy in Edge-Assisted Federated Learning over Industrial IoT

Federated learning (FL) is a key solution to realizing a cost-efficient and intelligent Industrial Internet of Things (IIoT). To improve training efficiency and mitigate the straggler effect of FL, this paper investigates an edge-assisted FL framework over an IIoT system by combining it with a mobile edge computing (MEC) technique. In the proposed edge-assisted FL framework, each IIoT device with weak computation capacity can offload partial local data to an edge server with strong computing power for edge training. In order to obtain the optimal offloading strategy, we formulate an FL loss function minimization problem under the latency constraint in the proposed edge-assisted FL framework by optimizing the offloading data size of each device. An optimal offloading strategy is first derived in a perfect channel state information (CSI) scenario. Then, we extend the strategy into an imperfect CSI scenario and accordingly propose a Q-learning-aided offloading strategy. Finally, our simulation results show that our proposed Q-learning-based offloading strategy can improve FL test accuracy by about 4.7% compared to the conventional FL scheme. Furthermore, the proposed Q-learning-based offloading strategy can achieve similar performance to the optimal offloading strategy and always outperforms the conventional FL scheme in different system parameters, which validates the effectiveness of the proposed edge-assisted framework and Q-learning-based offloading strategy.

Covert Beamforming Design for Integrated Radar Sensing and Communication Systems

We propose covert beamforming design frameworks for integrated radar sensing and communication (IRSC) systems, where the radar can covertly communicate with legitimate users under the cover of the probing waveforms without being detected by the eavesdropper. Specifically, by jointly designing the target detection beamformer and communication beamformer, we aim to maximize the radar detection mutual information (MI) (or the communication rate) subject to the covert constraint, the communication rate constraint (or the radar detection MI constraint), and the total power constraint. For the perfect eavesdropper’s channel state information (CSI) scenario, we transform the covert beamforming design problems into a series of convex subproblems, by exploiting semidefinite relaxation, which can be solved via the bisection search method. Considering the high complexity of iterative optimization, we further propose a single-iterative covert beamformer design scheme based on the zero-forcing criterion. For the imperfect eavesdropper’s CSI scenario, we develop a relaxation and restriction method to tackle the robust covert beamforming design problems. Simulation results demonstrate the effectiveness of the proposed covert beamforming schemes for perfect and imperfect CSI scenarios.

PERFORMANCE ANALYSIS OF THE NOMA NETWORK UNDER JOINT HARDWARE IMPAIRMENT AND IMPERFECT CHANNEL STATE INFORMATION

This paper investigates the effect of hardware impairments (HWIs) on non-orthogonal multiple access (NOMA) over a Rayleigh fading channel. We consider the practical scenario of imperfect channel state information, where the base station communicates directly with the NOMA users. In order to assess the performance of the proposed system, we derive expressions for the outage probability and the closed-form bit error rate. Through simulations, we validate the numerical results, which demonstrate that the presence of these imperfections jeopardizes the NOMA system. Additionally, the HWI affects the performance of users at varying levels due to the successive interference cancellation process. Moreover, the performance of the HWIs in the NOMA system is notably influenced by the accuracy of channel estimation.

MMSE Symbol Level Precoding Under a Per Antenna Power Constraint for Multiuser MIMO Systems With PSK Modulation

This letter proposes two MMSE symbol-level precoding approaches considering a strict per antenna power constraint and PSK modulation for perfect and imperfect channel state information scenarios. The proposed designs are formulated as second-order cone programs, allowing for an optimal solution via the interior point method. Numerical results confirm that, for the scenario with perfect channel state information, the proposed designs outperform the existing techniques in terms of bit-error-rate for low and intermediate signal-to-noise-ratio. Numerical evaluations also confirm the superiority of the proposed robust MMSE design in the presence of channel state information imperfection.

Millimeter Wave Communications With an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning

In this paper, a novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station, which is assisted by a reconfigurable intelligent reflector (IR). In particular, a channel estimation approach is developed to measure the channel state information (CSI) in real-time. First, for a perfect CSI scenario, the precoding transmission of the BS and the reflection coefficient of the IR are jointly optimized, via an iterative approach, so as to maximize the sum of downlink rates towards multiple users. Next, in the imperfect CSI scenario, a distributional reinforcement learning (DRL) approach is proposed to learn the optimal IR reflection and maximize the expectation of downlink capacity. In order to model the transmission rate’s probability distribution, a learning algorithm, based on quantile regression (QR), is developed, and the proposed QR-DRL method is proved to converge to a stable distribution of downlink transmission rate. Simulation results show that, in the error-free CSI scenario, the proposed approach yields over 30% and 2-fold increase in the downlink sum-rate, compared with a fixed IR reflection scheme and direct transmission scheme, respectively. Simulation results also show that by deploying more IR elements, the downlink sum-rate can be significantly improved. However, as the number of IR components increases, more time is required for channel estimation, and the slope of increase in the IR-aided transmission rate will become smaller. Furthermore, under limited knowledge of CSI, simulation results show that the proposed QR-DRL method, which learns a full distribution of the downlink rate, yields a better prediction accuracy and improves the downlink rate by 10% for online deployments, compared with a Q-learning baseline.

Joint Transmit and Receive Beamforming Design for Integrated Sensing and Communication

We consider the problem of the joint design of transmit and receive beamforming (TRBF) vectors for integrated sensing and communication systems in the signal-dependent interference scenario. The dual-functional base station can communicate with downlink users and detect targets simultaneously. By adopting the signal-to-interference-plus-noise ratio (SINR) as performance metrics for both radar and communication, we propose the joint design of the TRBF strategy to manifest the balance of radar and communication in both perfect and imperfect channel state information scenarios. Meanwhile, the upper bound performance of the SINR for target detection is analyzed. Numerical results show the SINR trade-offs between target detection and user communication.

Design and Performance Analysis of a New STBC-MIMO LoRa System

LoRa is a modulation technology for low power wide area networks (LPWAN) with enormous potential in 5G era. However, the performance of LoRa system deteriorates seriously in fading-channel environments. To tackle this problem, in this paper we introduce multiple-input-multiple-output (MIMO) configuration employing space-time block coding (STBC) schemes into the LoRa system to formulate an STBC-MIMO LoRa system. Then, we investigate the theoretical performance of the proposed system over Rayleigh fading channels. To this end, we derive the distribution of the decision metric for the demodulator in the proposed system. Based on the above distribution, we propose a closed-form approximate BER expression of the proposed system when perfect and imperfect channel state information (CSI) are considered. Furthermore, we analyze the diversity order and the throughput of the proposed system. The results of the diversity analysis demonstrate that the diversity order of the system in the imperfect CSI scenario with fixed channel estimate error variance is zero. However, in the imperfect CSI scenario with a decreasing channel estimate error variance and the perfect CSI scenario, the system can achieve full diversity. In addition, the results of the throughput analysis show that the throughput of the proposed system is little affected by CSI conditions. Simulation results verify the accuracy of the theoretical analysis and the excellent performance of the proposed system. Due to such superiority, the proposed STBC-MIMO LoRa system can be considered as a good scheme for LPWAN.

Graph traversal aided detection in uplink MBM massive MIMO based on socio‐cognitive knowledge of swarm optimization

SummaryMedia‐based modulation (MBM) plays a crucial role in enhancing the spectral efficiency and energy efficiency of massive MIMO (mMIMO) systems for 5G and beyond wireless communications. In MBM, multiple parasitic elements, also termed as radio frequency (RF) mirrors, are placed near the transmit antennas for generating different channel fade realizations. These realizations are obtained by ON/OFF switching of RF mirrors. One of those channel fade realizations is selected (using a part of the incoming information bits) for transmitting a part of the information bits utilizing a symbol chosen from the conventional constellation set (using another part of the incoming information bits). Transmission of a symbol through one of the available channel realizations constitutes a sparse transmit vector for each user in MBM‐mMIMO. The sparse nature of transmitted symbols from multiple users and inter‐user interference makes the symbol detection in uplink MBM‐mMIMO challenging. Therefore, in this article, the problem of symbol detection in MBM‐mMIMO is analyzed from a graph‐theoretical point of view, and a graph‐traversal aided low‐complexity symbol detection algorithm is proposed inspired by socio‐cognitive learning of swarm optimization. Also, the convergence characteristic of the proposed technique is investigated theoretically. Further, an analytical expression of upper bound on bit error rate performance is derived and corroborated through simulations. Viability and robustness of the proposed technique are also justified through simulations over state‐of‐art detection techniques, under both perfect and imperfect channel state information scenarios.

Performance Analysis of Downlink Non-Orthogonal Multiple Access under Imperfect CSI in Dense Network: A Stochastic Geometry Approach

A single-tier downlink Non-Orthogonal Multiple Access (NOMA) network is considered in this paper where analytical framework is given for performance evaluation. Particularly, the outage probability at the downlink users is studied under the impact of imperfect Channel State Information (CSI). The inter-cell interference and the intra-cell interference are also taken into consideration to capture practical behaviors of the proposed system. Further, we also adopt the Poisson Point Process (PPP) to model the location of the base stations and the downlink users in this work. For the imperfect CSI scenario, the results point out that the more times the user spends on performing interference cancellation, the more severe the user is affected by channel estimation error and vice versa. The results for the outage probability expressed in the analytical-forms are then confirmed by the Monte Carlo simulations.

Optimization of secure wireless communications for IoT networks in the presence of eavesdroppers

The problem motivates this paper is that securing the critical data of 5G based wireless IoT network is of significant importance. Wireless 5G IoT systems consist of a large number of devices (low-cost legitimate users), which are of low complexity and under strict energy constraints. Physical layer security (PLS) schemes, along with energy harvesting, have emerged as a potential candidate that provides an effective solution to address this issue. During the data collection process of IoT, PHY security techniques can exploit the characteristics of the wireless channel to ensure secure communication. This paper focuses on optimizing the secrecy rate for simultaneous wireless information and power transfer (SWIPT) IoT system, considering that the malicious eavesdroppers can intercept the data. In particular, the main aim is to optimize the secrecy rate of the system under signal to interference noise ratio (SINR), energy harvesting (EH), and total transmits power constraints. We model our design as an optimization problem that advocates the use of additional noise to ensure secure communication and guarantees efficient wireless energy transfer. The primary problem is non-convex due to complex objective functions in terms of transmit beamforming matrix and power splitting ratios. We have considered both the perfect channel state information (CSI) and the imperfect CSI scenarios. To circumvent the non-convexity of the primary problem in perfect CSI case, we proposed a solution based on the concave-convex procedure (CCCP) iterative algorithm, which results in a maximum local solution for the secrecy rate. In the imperfect CSI scenario, we facilitate the use of S-procedure and present a solution based on the iterative successive convex approximation (SCA) approach. Simulation results present the validations of the proposed algorithms. The results provide an insightful view that the proposed iterative method based on the CCCP algorithm achieves higher secrecy rates and lower computational complexity in comparison to the other algorithms.

Performance Analysis of a SAGE-Based Semi-Blind Channel Estimator for Pilot Contaminated MU Massive MIMO Systems

Massive multiple-input multiple-output (M-MIMO) is one of the key ingredients in the upcoming 5G technology. The benefits associated with M-MIMO rely largely on the accuracy of channel state information (CSI) available at the base station (BS). The existing literature mostly employs pilot-aided schemes which require additional pilots for improving their CSI accuracy; additionally, pilot contamination degrades their performance to a large extent. In this paper, we design a Space-Alternating Generalized Expectation-Maximization (SAGE) based semi-blind estimator for pilot contaminated multi-user (MU) M-MIMO systems. It utilizes both pilot and a few data symbols for CSI estimation through two stages, namely initialization and iterative SAGE (ISAGE). We obtain an initial channel estimate with the help of a pilot-aided linear minimum mean squared error (LMMSE) estimator in the initialization stage. The acquired initial estimate from the former stage is then iteratively updated by SAGE algorithm with the joint usage of pilot and a few data symbols in ISAGE stage. The inclusion of data information in ISAGE stages' estimation process aids in simultaneous improvement of CSI accuracy and spectral efficiency (SE) of a M-MIMO system; which is unlikely for a pilot based estimation scheme. Through simulations, we show that our estimator obtains a considerable improvement over the existing pilot-aided schemes in terms of mean squared error (MSE), bit error rate (BER), SE, and energy efficiency (EE) at a nominal increase in complexity. Besides, on average, it achieves convergence in almost two iterations. We also derive modified Cramer-Rao lower bound (MCRLB) to validate the estimation efficacy of our estimator. We evaluate a closed-form expression for lower bound on UL achievable rate of MU M-MIMO systems under both perfect and imperfect CSI scenario. We also discuss the trade-off between SE and EE.

Delay-Optimal Resource Scheduling of Energy Harvesting-Based Devices

This paper investigates resource scheduling in a wireless communication system operating with energy harvesting (EH)-based devices and perfect channel state information (CSI). The aim is to minimize the packet loss that occurs when the buffer is overflowed or when the queued packet is older than a certain pre-defined threshold. So, we consider a strict delay constraint rather than an average delay constraint. The associated optimization problem is modeled as the Markov decision process (MDP) where the actions are the number of packets sent on the known channel at each slot. The optimal deterministic offline policy is exhibited through dynamic programming techniques, i.e., value iteration (VI) algorithm. We show that the gain in the number of transmitted packets and the consumed energy is substantial compared to: 1) a naive policy which forces the system to send the maximum number of packets using the available energy in the battery; 2) two variants of the previous policy that take into account the buffer state; and 3) a policy optimized with an average delay constraint. Finally, we evaluate our optimal policy under imperfect CSI scenario where only an estimate of the channel state is available.

On the Energy Efficiency of Interference Alignment in the $K$ -User Interference Channel

Interference alignment (IA) is regarded as an important physical-layer interference management technique. Most research contributions on IA were focused on the analysis of its achievable spectral efficiency, either from the degrees of freedom or from the capacity perspective. Meanwhile, high energy-efficiency (EE) has become a key requirement for next-generation wireless communications. Hence, we focus our attention on the EE of IA in the fully connected $K$ -user interference channel, where each user has either a single antenna or multiple antennas. We consider both perfect and imperfect channel state information scenarios. New insights into the achievable EE of IA are obtained by investigating the impact of different precoding matrices, the number of users, the number of antennas, symbol extension values, channel estimation accuracy, total transmit power, and power allocation schemes. In particular, we demonstrate that in the single-antenna-user case, the IA schemes relying on unequal power allocation achieves higher EE than their equal power allocation counterparts. However, in the scenario where each user is equipped with multiple antennas, equal power allocation achieves higher EE than unequal power allocation for the IA. Furthermore, using non-uniform precoding-matrix-generating vector ${\boldsymbol {w}}$ is not necessarily beneficial for improving the achievable EE of IA. We also find that the EE performance of IA with smaller symbol extension values is higher than that with larger symbol extension values, the achievable EE of IA decays with the increase of the total transmit power, the EE performance of IA degrades as the channel estimation accuracy becomes low and that having a larger number of transmit/receive antennas on each user achieves a higher EE in IA.

On the Performance of Wireless Powered Cognitive Relay Network With Interference Alignment

In this paper, a two-hop decode-and-forward wireless powered relaying cognitive radio network with interference alignment over Rayleigh fading channels is investigated. The energy-constrained secondary relay node harvests energy from both the information and interference signals. Then, the harvested energy is used by the relay to forward the information signal from the source to the destination node. By applying beamforming matrices to primary and secondary networks, the performance metrics, such as outage probability, capacity, and bit error rate are studied under perfect and imperfect channel state information scenarios for both power-splitting relaying (PSR) and time-switching relaying (TSR) protocols. In addition, the optimal network performance is achieved by calculating the optimal energy harvesting time-switching and power-splitting coefficients. Finally, closed-form expressions for the outage probability of primary and secondary users are derived. Monte Carlo simulation results corroborate the analytical ones and show that the PSR technique outperforms TSR in the considered system model.

Power Scaling Laws of Massive MIMO Full-Duplex Relaying With Hardware Impairments

This paper considers a massive MIMO full-duplex relaying (FDR) system, in which multiple single-antenna sources simultaneously communicate with multiple single-antenna destinations using a single relay that is equipped with $N_{\mathrm {tx}}$ transmit antennas and $N_{\mathrm {rx}}$ receive antennas. Under the practical scenario of imperfect channel-state information, the relay processes the received signals by means of maximum-ratio combining/maximum-ratio transmission (MRC/MRT) or zero-forcing (ZF) processing, and employs either the decode-and-forward (DF) or amplify-and-forward (AF) scheme. Considering hardware impairments, closed-form expressions of the lower bounds on the sum spectral efficiencies for DF and AF schemes are derived for both the MRC/MRT and ZF processing methods. Based on the obtained expressions, various power scaling laws are established to show the relationships among the transmit powers of the sources, relay, and pilots in order to maintain a desirable quality of service when $N_{\mathrm {tx}}$ and $N_{\mathrm {rx}}$ go to infinity but with a fixed ratio. In particular, it is found that the massive MIMO FDR systems under consideration are not affected by the loop interference, can save power, and improve the rate performance when the three transmit powers are scaled down to ${1/ {N_{\mathrm {rx}}^{a}}}$ , ${1 / {N_{\mathrm {tx}}^{b}}}$ , and ${1 / {N_{\mathrm {rx}}^{c}}}$ , respectively, where $a + c = 1$ , $b + c , and $b > 0$ . Numerical results corroborate the accuracy of the closed-form expressions and show that, when the loop interference level is small, using low-quality hardware at the relay and high-quality hardware at the sources and the destinations is a good design choice in the practical design of low-cost massive MIMO FDR systems.

Joint Prioritized Scheduling and Resource Allocation for OFDMA-Based Wireless Networks

In this paper, we study the joint prioritized link scheduling and resource allocation for OFDMA-based wireless networks, which serve two classes of wireless links, namely, non-prioritized (low-priority) and prioritized (high-priority) links. Our design aims to maximize the number of scheduled non-prioritized links and their sum rate, while guaranteeing the minimum required rates of all active prioritized and non-prioritized links. We present the problem formulation as a single-stage optimization problem, which simultaneously maximizes the number of scheduled non-prioritized links and their sum rate. We propose a monotonic-based optimal approaching (MBOA) algorithm to solve this problem by employing the monotonic global optimization technique and an efficient rounding procedure. We prove that the MBOA algorithm can schedule the maximum number of non-prioritized links with slight and controllable degradation in the minimum required rates of non-prioritized links. For low-complexity design, we propose an iterative convex approximation algorithm, which sequentially performs power allocation and link removal in each iteration. We then describe how the proposed algorithms can be implemented in the standardized LTE-based cellular system. Finally, we conduct numerical studies for device-to-device communications underlaid cellular networks under perfect or imperfect channel state information (CSI). Numerical results demonstrate that the proposed algorithms can be applied to the imperfect CSI scenario with slight degradation in the network performance. Moreover, in the perfect CSI scenario, the proposed algorithms significantly outperform the conventional algorithms both in the number of scheduled non-prioritized links and their sum rate.