Channel Distribution Information Research Articles

Knowledge graph completion assisted graph neural network for spectrum resource optimization of UAV swarm

Unmanned aerial vehicles (UAVs) are playing an increasing critical role in industrial, agricultural and military Scenario. However, the energy consuming cannot be ignored, which constrains the flying time and quality of service (QoS), especially in large UAV swarm. In this paper, a knowledge graph completion assisted graph neural network (KGC-GNN) is proposed for UAV swarm aiming at optimizing the spectrum resource to improve the energy efficiency (EE). The proposed network consists of two parts: a graph attention network based on relational coding (GATRC) encoder and a task-oriented decoder based on InteractE model named CP-IE. The further simulation demonstrates that our approach can attain near-optimal spectrum resource optimization scheme with higher energy efficiency and achieve rapid convergence with the channel distribution information (CDI), UAV and environment parameters.

Energy-Efficient Coordinated Beamforming in Multi-Pair MISO Networks With CDI and Eavesdroppers

This paper investigates the energy-efficient coordinated beamforming design for multi-pair multiple-input single-output (MISO) networks with passive eavesdroppers. To be practical, it is assumed that only channel distribution information (CDI) of the network is known by the transmitters/sources, and the dynamic energy consumption model (DECM) is employed. In order to achieve a green network design, an energy efficiency (EE) maximization problem is formulated subjecting to the individual available power constraints, the rate outage probability constraints, and the information leakage probability constraints. To solve the formulated non-convex problem, semidefinite relaxation (SDR) and first-order lower bound are applied to transform the problem, and then an efficient algorithm is proposed based on successive convex approximation (SCA) and Dinkelbach's approaches. The proposed algorithm is theoretically proved to converge to a stationary point of the considered problem. Further, a distributed version of the proposed algorithm is designed, with which each transmitter is able to optimize its own beamforming vector with local CDI. Moreover, the computational complexities and the signaling overheads of the two developed algorithms are analyzed and compared. Simulation results show that both algorithms achieve good EE performance, and the EE performance achieved by the distributed algorithm is very similar to that achieved by the centralized one. Additionally, it is shown that similar to the conventional scenarios without eavesdroppers, the achieved system EE also has a saturation point w.r.t. the available power of the transmitters, and by employing our proposed algorithms, the network security is significantly enhanced.

Covert Communication in Uplink NOMA Systems Under Channel Distribution Information Uncertainty

It is challenging to covert communication under channel distribution information (CDI) uncertainty caused by limited feedback and rapid-changing channels. This letter investigates covert communication in uplink non-orthogonal multiple access (NOMA) systems under CDI uncertainty, where a covert user (CU) and reliable user (RU) transmit messages to a legitimate receiver (Bob) threatened by a warden (Willie). RU not only transmits its public messages but also helps CU to send private messages against Willie’s detection. To protect CU’s transmission with imperfect CDI, we first study the worst case for NOMA users and obtain a lower bound of the worst-case detection error probability (DEP). In addition, both closed-form expressions for RU and CU’s connection outage probabilities are derived. Constrained by the DEP and connection outage probability, we maximize the expected covert rate (ECR) and derive the optimal power settings for RU and CU under imperfect CDI. Numerical results show that the proposed robust power setting scheme achieves a positive maximum ECR despite CDI uncertainty causing the reduction of covertness performance.

Minimizing age of information in the uplink multi-user networks via dynamic bandwidth allocation

Improving the information freshness is critical for the monitoring and controlling applications in the cellular Internet of Things (IoT). In this paper, we are interested in optimizing the bandwidth allocation dynamically to improve the information freshness of the short packet based uplink status updates, which is characterized by a recently proposed metric, age of information (AoI). We first design a status update scheme with channel distribution information (CDI). By relaxing the hard bandwidth constraint and introducing a Lagrangian multiplier, we first decouple the multi-MTCD bandwidth allocation problem into a single MTCD Markov decision process (MDP). Under the MDP framework, after variable substitution, we obtain the single-MTCD status update scheme by solving a linear programming problem. Then, we adjust the Lagrangian multiplier to make the obtained scheme satisfy the relaxed bandwidth constraint. Finally, a greedy policy is built on the proposed scheme to adjust the bandwidth allocation in each slot to satisfy the hard bandwidth constraint. In the unknown environment without CDI, we further design a bandwidth allocation scheme which only maximizes the expected sum AoI drop within each time slot. Simulation results show that in terms of AoI, the proposed schemes outperform the benchmark schemes.

Efficient User Scheduling for Uplink Hybrid Satellite-Terrestrial Communication

Due to increasing demands of seamless connection and massive information exchange across the world, the integrated satellite-terrestrial communication systems develop rapidly. To shed lights on the design of this system, we consider an uplink communication model consisting of a single satellite, a single terrestrial station and multiple ground users. The terrestrial station uses decode-and-forward (DF) to facilitate the communication between ground users and the satellite. The channel between the satellite and the terrestrial station is assumed to be a quasi-static shadowed Rician fading channel, while the channels between the terrestrial station and ground users are assumed to experience independent quasi-static Rayleigh fading. We consider two cases of channel state information (CSI) availability. When perfect CSI is available, we derive the instantaneous achievable sum rate of all ground users and formulate an optimization problem to maximize the sum rate. When only channel distribution information (CDI) is available, we derive a closed-form expression for the outage probability and formulate another optimization problem to minimize the outage probability. Both optimization problems correspond to scheduling algorithms for ground users. For both cases, we propose low-complexity user scheduling algorithms and demonstrate the efficiency of our scheduling algorithms via numerical simulations.

Maximum Likelihood Detection in Single-Input Double-Output Non-Gaussian Barrage-Jammed Systems

We derive the likelihood functions and the maximum likelihood (ML) detectors for four classes of single-input double-output (SIDO) communication systems, i.e., systems with one transmit and two receive antennas. For all classes, the received signals are contaminated by a Gaussian noise component and a non-Gaussian component induced by the Gaussian transmissions of a proactive continuous single-antenna jammer over an unknown complex <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2\times 1$</tex-math></inline-formula> Gaussian vector channel. The considered classes correspond to whether full channel distribution information (CDI), or partial CDI about the transmitter channel and the jammer channel is available at the receiver. Unlike their scalar counterparts, the vector channels considered herein interweave the components of the received signal, rendering the derivation of the likelihood function a daunting task for more than two receive antennas. Furthermore, the interweaving of the received signal components in the vector channel case prevents the optimal ML detector for unit-norm constellations from reducing to the corresponding Gaussian approximation-based detector. This is in sharp contrast with the scalar case, wherein the two detectors are equivalent for unit-norm constellations. Confirming our analytical findings, experimental results show that the difference between the two detectors can be significant, especially when the transmitter-receiver and jammer-receiver channels have substantial line-of-sight components. Although the computational cost of performing optimal ML detection in the presence of non-Gaussian jamming is higher in the case of two receive antennas, the performance advantage over the single antenna case justifies it.

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> probability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.

MD-GAN-Based UAV Trajectory and Power Optimization for Cognitive Covert Communications

This article investigates the covert performance of an unmanned aerial vehicle (UAV) jammer-assisted cognitive radio (CR) network. In particular, the covert transmission of secondary users can be effectively protected by UAV jamming against the eavesdropping. For practical consideration, the UAV is assumed to only know certain partial channel distribution information (CDI), whereas not to know the detection threshold of an eavesdropper. For this sake, we propose a model-driven generative adversarial network (MD-GAN)-assisted optimization framework, consisting of a generator and a discriminator, where the unknown channel information and the detection threshold are learned weights. Then, a GAN-based joint trajectory and power optimization (GAN-JTP) algorithm is developed to train the MD-GAN optimization framework for covert communication, which results in the joint solution of the UAV’s trajectory and transmits power to maximize the covert rate and the probability of detection errors. Our simulation results show that the proposed GAN-JTP with a rapid convergence speed can attain near-optimal solutions of the UAV’s trajectory and transmit power for the covert communication.

Noncoherent Massive Random Access for Inhomogeneous Networks: From Message Passing to Deep Learning

Massive machine-type communications (mMTC) are expected to support a large amount of randomly deployed users for short package transmissions. Noncoherent random access provides an efficient and practical multi-access protocol for mMTC, and also poses new challenges for the receiver design. In this paper, we leverage two well-known methods, i.e., message passing and deep learning, to jointly detect the user activity and the desired data for the noncoherent mMTC. First, by exploiting the exact distribution information of the received signal, a generalized approximate message passing (GAMP)-based algorithm is proposed, which is shown to jointly detect the user activity and the desired data by two modules: inter-user interference elimination and data detection for each user. Inspired by the two-module GAMP-based algorithm, we then propose a model-driven deep learning method, which utilizes the deep neural networks (DNNs) to approximate both the two modules. The loss function for training the DNNs is derived by formulating the two-module detection as an unconstrained optimization problem. Simulation results reveal that the proposed GAMP-based algorithm outperforms the proposed deep learning method when the channel distribution is perfectly known, while it suffers from a significant performance degradation for the case with imperfect channel distribution information.

Age-Energy Tradeoff Optimization for Packet Delivery in Fading Channels

Power control policies that minimize the weighted combination of age of information (AoI) and total energy consumption are studied in this paper. It is assumed that the status update information is acquired at the transmitter through sensors and then sent in packets to the receiver at fixed rate over Rayleigh fading channels. Retransmission mechanism is introduced to guarantee the reliability of the received update packets, and a limit on the maximum number of transmission rounds is imposed. On the one hand, with the channel distribution information (CDI) available at the transmitter, the age-energy tradeoff is optimized by formulating the problem as a constrained Markov decision process (CMDP) which takes into account the sensing power consumption as well as the average transmission power constraint. Employing the Lagrangian relaxation, the CMDP problem is transformed into an unconstrained Markov decision process (MDP) problem. A threshold-based policy is also provided. Subsequently, a dynamic programming approach is proposed to obtain the optimal power allocation policy. On the other hand, when the environment is unknown, two probabilistic reinforcement learning algorithms are proposed to optimize the age-energy tradeoff. Through simulation results, it is shown that the age-energy tradeoffs can be improved by the proposed optimal policies compared with benchmark schemes.

Cell-Free Massive MIMO Systems With Oscillator Phase Noise: Performance Analysis and Power Control

Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising technology for satisfying higher rate requirement of users in beyond-5G networks. This paper investigates the impact of phase noise on the performance of cell-free MIMO systems which employ a large number of multi-antenna access points (APs). For both downlink and uplink transmissions, we derive the tractable closed-form achievable rate expressions when signal detection with channel state information (CSI) and channel distribution information (CDI), respectively, by utilizing the extended deterministic equivalence method in random matrix theory. The closed-form achievable rate expressions provide efficient evaluation for the impact of phase noise on system performance. The analytical results indicate that the phase noise influences the power of the desired signal, the beamforming uncertainty gain, and the inter-user interference increment caused by the pilot contamination. In addition, the special cases that only APs have phase noise and only users have phase noise are discussed. It is found that the impact of phase noise at users is more severe than that at APs. Besides, we discuss the system performance at low and high SNR regimes. It demonstrates that the phase noise is the major limitation of the system performance in high SNR regime while it can be neglected in low SNR regime. Furthermore, we formulate the max-min power control problem for both downlink and uplink transmissions to guarantee the users’ fairness and prove that the downlink max-min problem is a quasi-concave problem while the uplink max-min problem is a geometric programming (GP) problem. Then, the phase noise-aware max-min power control schemes for both downlink and uplink transmissions are proposed to ensure the users’ fairness. Finally, numerical results are provided to validate the analytical results and the proposed phase noise-aware power control scheme.

Robust Power Allocation in Covert Communication: Imperfect CDI

The study of the fundamental limits of covert communications, where a transmitter Alice wants to send information to a desired recipient Bob without detection of that transmission by an attentive and capable warden Willie, has emerged recently as a topic of great research interest. Previous work has assumed that the distribution of the multipath fading on the channel, which we term the channel distribution information (CDI), is known to Alice, hence facilitating her characterization of Willie's capabilities to detect the signal. However, in practice, Willie tends to be passive and the environment heterogeneous, implying a lack of signaling interchange between the transmitter and Willie makes it difficult if not impossible for Alice to estimate the CDI exactly and provide covertness guarantees. In this paper, we address this issue by developing covert communication schemes for various assumptions on Alice's imperfect CDI: 1) when the transmitter knows the channel distribution is within some distance of a nominal distribution; 2) when only the mean and variance of the channel distribution are available; 3) when Alice knows the channel distribution is complex Gaussian but the variance is unknown. In each case, we formulate new optimization problems to find the power allocations that maximize covert rate subject to a covertness requirement under uncertain CDI. Moreover, Willie may be able to estimate CDI exactly or be faced with an error in estimation, and we investigate two these issues. Numerical results are presented to compare the proposed schemes from various aspects, for attaining desirable covert system performance.

Detect Pilot Spoofing Attack for Intelligent Reflecting Surface Assisted Systems

Pilot spoofing attack (PSA) is a kind of active eavesdropping, which launches the identical training sequence to manipulate the channel estimation outcome during the pilot training phase. The newly proposed intelligent reflecting surface (IRS) system is vulnerable to the PSA because it relies on the pilot-assisted channel estimation methods to obtain channel statement information (CSI). To combat PSA in IRS-assisted systems, we consider two significant challenges: 1) channel distribution information (CDI) is uncertain, which will make the PSA detection methods based on the statistic feature (SF) invalid; 2) the noise is uncertain when detecting PSA in practical scenarios, which would impair the popular energy-based detectors. In this paper, we design a three-step training (TST) scheme. The effective detector could achieve reliable detection performance by examining the transmitter's received signal power levels when PSA occurs. The proposed method does not require the prior knowledge of noise variance and could obtain the CSI of both legitimate and illegitimate dyadic backscatter channels. Theoretical analysis and numerical simulations are presented further to demonstrate the efficiency of our proposed TST scheme.

Non-Orthogonal Multiple Access in Distributed Antenna Systems for Max-Min Fairness and Max-Sum-Rate

The distributed antenna systems (DAS) and non-orthogonal multiple access (NOMA) are the key technologies to boost the data rate of the cellular system in terms of small cell and multiple access, respectively. To meet the high data rate requirements for 5G and beyond, we suggest a framework of using NOMA in DAS. In the proposed scheme, RRUs which are geographically distributed in the cell serve the cell-edge users within their own coverage. Meanwhile, the macro BS, which covers the entire cell region with relatively high transmit power, supports both the cell-center and the cell-edge users with identical resources by using NOMA. Compared to the conventional DAS where the macro BS and the RRU serve the cell-center and the cell-edge users, respectively, the proposed framework also boosts the data rate of the cell-center user by improving the reliability of successive interference cancellation (SIC) at the cell-center user. In the proposed framework, this paper also proposes the optimal power allocation rules maximizing the user fairness in two different cases of instantaneous channel gain information (CGI) and channel distribution information (CDI) known at the transmitter. Also, the power allocation methods maximizing the sum-rate with a minimum rate constraint and the weighted sum-rate are presented in the case of CGI known at the transmitter. Simulation results show that the proposed framework of using NOMA in DAS can boost data rates more in a variety of system environments compared to the conventional NOMA or DAS.

Physical Layer Security Aided Wireless Interference Networks in the Presence of Strong Eavesdropper Channels

Under both long (infinite) and short (finite) blocklength transmissions, this paper considers physical layer security for a wireless interference network of multiple transmitter-user pairs, which is overheard by multiple eavesdroppers (EVs). The EVs are assumed to have better channel conditions than the legitimate users (UEs), making the conventional transmission unsecured. The paper develops a novel time-fraction based transmission, under which the information is transmitted to the UEs within a fraction of the time slot and artificial noise (AN) is transmitted within the remaining fraction to counter the strong EVs’ channels. Based on channel distribution information of UEs and EVs, the joint design of transmit beamforming, time fractions and AN power allocation to maximize the worst users’ secrecy rate is formulated in terms of nonconvex problems. Path-following algorithms of low complexity and rapid convergence are proposed for their solution. Simulations are provided to demonstrate the viability of the proposed methodology.

Covert Wireless Communications Under Quasi-Static Fading With Channel Uncertainty

Covert communications enable a transmitter to send information reliably in the presence of an adversary, who looks to detect whether the transmission took place or not. We consider covert communications over quasi-static block fading channels, where users suffer from channel uncertainty. We investigate the adversary Willie’s optimal detection performance in two extreme cases, i.e., the case of perfect channel state information (CSI) and the case of channel distribution information (CDI) only. It is shown that in the large detection error regime, Willie’s detection performances of these two cases are essentially indistinguishable, which implies that the quality of CSI does not help Willie in improving his detection performance. This result enables us to study the covert transmission design without the need to factor in the exact amount of channel uncertainty at Willie. We then obtain the optimal and suboptimal closed-form solution to the covert transmission design. Our result reveals fundamental difference in the design between the case of quasi-static fading channel and the previously studied case of non-fading AWGN channel.

Throughput Maximization With an Average Age of Information Constraint in Fading Channels

In emerging fifth generation and beyond wireless communication systems, communication nodes are expected to support information flows that are freshness-sensitive , along with broadband traffic having high data rate requirements. Freshness-sensitive flows, where freshness is quantified by a metric called the age of information (AoI), are naturally assigned priority over resources. Motivated by this, we consider long-term average throughput maximization in a single user fading channel, subject to constraints on average AoI and power, and knowledge of channel state information at the transmitter (CSIT), which is the realization of channel power gains. We consider two scenarios: (i) when Perfect CSIT is available and (ii) when CSIT is not available. In both scenarios, the channel distribution information is available. We consider a generate-at-will model, in which update packets can be generated in any block of interest, at the transmitter. We propose simple age-independent stationary randomized policies (AI-SRP), which allocate powers at the transmitter based only on the channel state and/or distribution information, without any knowledge of the AoI. We show that the optimal long-term average throughputs achieved by the AI-SRPs are equal to at least half of the throughputs achieved by optimal policies, independent of all the parameters of the problem. Furthermore, we provide an expression that bounds the difference in throughputs achieved by the optimal policies and AI-SRPs. Finally, we provide extensive numerical results to illustrate the performance of AI-SRPs.

SWIPT-Enabled Full-Duplex NOMA Networks With Full and Partial CSI

This paper investigates the simultaneous wireless information and power transfer (SWIPT)-enabled cooperative non-orthogonal multiple access (NOMA) with full-duplex (FD) relaying. Two optimization problems are formulated to minimize the required transmit power with both full channel state information (CSI) and partial CSI (i.e., channel distribution information (CDI)). As both problems are non-convex without explicit solution methods, the semidefinite relaxation (SDR) is applied to relax them firstly. Then, for the full CSI case, a bilevel programming (BLP)-based method is presented to find the global optimal solution, and as an alternative, a successive convex approximation (SCA)-based method with arithmetic and geometric means (AGM) inequality is designed to achieve the suboptimal solution with low complexity. Moreover, in a special case with single transmit antenna, the closed-form expression of the optimal power allocation is derived for a given power splitting (PS) ratio and then the global optimal solution is found by using Bisection method. For the CDI case, the first-order Taylor expansion is adopted to approximate the non-convex constraints and then a SCA-based algorithm is designed to find the suboptimal solution. Numerical results validate our obtained theoretical results and proposed solution methods, and also show that by migrating FD relaying into SWIPT-enabled cooperative NOMA, a considerable system performance gain can be achieved for both full CSI and CDI cases. Besides, we also discuss the gain brought by NOMA and FD separately and the effect of the relay location on the system performance. It shows that FD may bring more contribution to the system performance improvement than NOMA.

Energy-Efficient Data Uploading for Cellular-Connected UAV Systems

Integrating unmanned aerial vehicles (UAVs) into cellular networks offers a promising solution to support their efficient operations and achieve high-quality communication with the ground. In this paper, we consider a cellular-connected UAV communication system, in which one energy-constrained UAV flies from a given initial location to a final location while uploading data to the ground base stations (GBSs) along its flight. We study the joint design of UAV operation time, communication scheduling, as well as UAV trajectory and transmit power to maximize the data uploading throughput, subject to the communication quality of service (QoS) requirement and UAV energy budget constraints. We first consider an offline design approach by utilizing only the channel distribution information (CDI) that is available prior to the UAV's flight, which is formulated as a non-convex optimization problem and challenging to solve. By using path disretization and successive convex approximation (SCA) techniques, an efficient alternating optimization algorithm is proposed, which can converge to a solution that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Then, we further study the online design approach by utilizing the instantaneous channel state information (CSI) that is available to the UAV in real time along its flight. As the online design problem has similar structure as that of the offline design, an adaptive online optimization algorithm is proposed. To further reduce the computational complexity, a low-complexity online algorithm based on receding horizon optimization (RHO) is developed by utilizing a combined offline and online design approach. Simulations are conducted to corroborate our study and the results demonstrate the performance gain of proposed designs as compared to various baseline schemes. Furthermore, our results unveil the tradeoff between system throughput and UAV endurance in the considered system.

Secrecy Energy Efficiency in Multi-Antenna SWIPT Networks With Dual-Layer PS Receivers

This paper studies the secrecy energy efficiency (SEE) for MISO power-splitting (PS) SWIPT networks in the presence of multiple passive eavesdroppers (Eves), where the non-linear energy harvesting (EH) model and the dual-layer PS receiver architecture are employed. With only channel distribution information (CDI) of Eves known and the artificial noise (AN) embedded into the transmit signals at the transmitter, a SEE maximization problem is formulated under constraints of the minimal rate and EH requirements of legitimate receivers and the power budget at the transmitter. To tackle the difficulty caused by the fractional objective function and the probability constraints in solving the considered problem, the second-layer PS ratios are firstly optimized by bisection and sum-of-ratios maximization methods, and then the transmit beamforming vectors, the AN covariance matrix and the first-layer PS ratios are jointly optimized by using successive convex approximation (SCA) and Dinkelbach’s methods. The proposed solution approach is theoretically proved to converge to a stationary point of the SDR form of the considered problem, which is further shown to be the optimal one. Numerical results show that our proposed design achieves the highest SEE over traditional power minimization and secrecy rate maximization designs. Moreover, when the rate requirement is larger than a threshold or the available power is less than a threshold, traditional power minimization design or secrecy rate maximization design is able to achieve a similar SEE to our proposed design. Besides, the dual-layer PS receiver architecture is able to improve the EH efficiency and system SEE.