Achieving Covert Communication With A Probabilistic Jamming Strategy

In this work, we consider a covert communication scenario, where a transmitter Alice communicates to a receiver Bob with the aid of a probabilistic and uninformed jammer against an adversary warden's detection. The transmission status and power of the jammer are random and follow some priori probabilities. We first analyze the warden's detection performance as a function of the jammer's transmission probability, transmit power distribution, and Alice's transmit power. We then maximize the covert throughput from Alice to Bob subject to a covertness constraint, by designing the covert communication strategies from three different perspectives: Alice's perspective, the jammer's perspective, and the global perspective. Our analysis reveals that the minimum jamming power should not always be zero in the probabilistic jamming strategy, which is different from that in the continuous jamming strategy presented in the literature. In addition, we prove that the minimum jamming power should be the same as Alice's covert transmit power, depending on the covertness and average jamming power constraints. Furthermore, our results show that the probabilistic jamming can outperform the continuous jamming in terms of achieving a higher covert throughput under the same covertness and average jamming power constraints.

A New Heterogeneous Hybrid MIMO Receive Structure of Rapidly Eliminating DOA Ambiguity

Massive multiple input multiple output(MIMO)-based fully-digital receive antenna arrays eventuate a huge amount of circuit costs and complexity to direction of arrival(DOA) estimation, which is hard to satisfy the needs of high precision and low cost in future green wireless communication. To address this challenge, a novel heterogeneous hybrid MIMO receiver is proposed in this paper and a high performance DOA estimator called heterogeneous cross-minimum distance (HCMD) is developed based on the structure. The antenna arrays are first divided into multiple groups, and each group adopts a different hybrid structure. The virtual antenna arrays of these groups are then used for DOA estimation to generate multiple candidate angle sets, where each set contains a unique true solution and multiple pseudo-solutions. Finally, the cross-distance minimization method is applied to the multiple candidate angle sets to select the corresponding true solution for each group, and the final DOA estimation is given by combining the multiple true solutions. Simulation results show that as the number of antennas tends to large-scale, the proposed method can rapidly find the true solution for each group and achieve excellent estimation performance.

Asymptotic Performance Analysis of Large-Scale Active IRS-Aided Wireless Network

In this paper, the dominant factor affecting the performance of active intelligent reflecting surface (IRS) aided wireless communication networks in Rayleigh fading channel, namely the average signal-to-noise ratio (SNR) $\gamma_0$ at IRS, is studied. Making use of the weak law of large numbers, its simple asymptotic expression is derived as the number $N$ of IRS elements goes to medium-scale and large-scale. When $N$ tends to large-scale, the asymptotic received SNR at user is proved to be a linear increasing function of a product of $\gamma_0$ and $N$. Subsequently, when the BS transmit power is fixed, there exists an optimal limited reflective power at IRS. At this point, more IRS reflect power will degrade the SNR performance. Additionally, under the total power sum constraint of the BS transmit power and the power reflected by the IRS, an optimal power allocation (PA) strategy is derived and shown to achieve 0.83 bit rate gain over equal PA. Finally, an IRS with finite phase shifters being taken into account, generates phase quantization errors, and further leads to a degradation of receive performance. The corresponding closed-form performance loss expressions for user's asymptotic SNR, achievable rate (AR), and bit error rate (BER) are derived for active IRS. Numerical simulation results show that a 3-bit discrete phase shifter is required to achieve a trivial performance loss for a large-scale active IRS.

Beamforming Design for IRS-and-UAV-aided Two-way Amplify-and-Forward Relay Networks

As a promising solution to improve communication quality, unmanned aerial vehicle (UAV) has been widely integrated into wireless networks. In this paper, for the sake of enhancing the message exchange rate between User1 (U1) and User2 (U2), an intelligent reflective surface (IRS)-and-UAV- assisted two-way amplify-and-forward (AF) relay wireless system is proposed, where U1 and U2 can communicate each other via a UAV-mounted IRS and an AF relay. Besides, an optimization problem of maximizing minimum rate is casted, where the variables, namely AF relay beamforming matrix and IRS phase shifts of two time slots, need to be optimized. To achieve a maximum rate, a low-complexity alternately iterative (AI) scheme based on zero forcing and successive convex approximation (LC-ZF-SCA) algorithm is put forward, where the expression of AF relay beamforming matrix can be derived in semi-closed form by ZF method, and IRS phase shift vectors of two time slots can be respectively optimized by utilizing SCA algorithm. To obtain a significant rate enhancement, a high-performance AI method based on one step, semidefinite programming and penalty SCA (ONS-SDP-PSCA) is proposed, where the beamforming matrix at AF relay can be firstly solved by singular value decomposition and ONS method, IRS phase shift matrices of two time slots are optimized by SDP and PSCA algorithms. Simulation results present that the rate performance of the proposed LC-ZF-SCA and ONS-SDP-PSCA methods surpass those of random phase and only AF relay. In particular, when total transmit power is equal to 30dBm, the proposed two methods can harvest more than 68.5% rate gain compared to random phase and only AF relay. Meanwhile, the rate performance of ONS-SDP-PSCA method at cost of extremely high complexity is superior to that of LC-ZF-SCA method.

STAR-RIS-UAV Aided Coordinated Multipoint Cellular System for Multi-user Networks

Different with conventional reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) can reflect and transmit the signals to the receiver. In this paper, to serve more ground users and increase the deployment flexibility, we investigate an unmanned aerial vehicle equipped with a STAR-RIS (STAR-RIS-UAV) aided wireless communications for multi-user networks. Energy splitting (ES) and mode switching (MS) protocols are considered to control the reflection and transmission coefficients of STAR-RIS elements. To maximize the sum rate of the STAR-RIS-UAV aided coordinated multipoint cellular system for multi-user networks, the corresponding beamforming vectors as well as transmitted and reflected coefficients matrices are optimized. Specifically, instead of adopting the alternating optimization, we design an iteration method to optimize all variables for both ES and MS protocols at the same time. Simulation results reveal that STAR-RIS-UAV aided wireless communication system has a much higher sum rate than the system with conventional RIS or without RIS. Furthermore, the proposed structure is more flexible than a fixed STAR-RIS and could greatly promote the sum rate.

Over-the-Air Federated Averaging with Limited Power and Privacy Budgets

To jointly overcome the communication bottleneck and privacy leakage of wireless federated learning (FL), this paper studies a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system with a limited sum power budget. With DP-OTA-FedAvg, the gradients are aligned by an alignment coefficient and aggregated over the air, and channel noise is employed to protect privacy. We aim to improve the learning performance by jointly designing the device scheduling, alignment coefficient, and the number of aggregation rounds of federated averaging (FedAvg) subject to sum power and privacy constraints. We first present the privacy analysis based on differential privacy (DP) to quantify the impact of the alignment coefficient on privacy preservation in each communication round. Furthermore, to study how the device scheduling, alignment coefficient, and the number of the global aggregation affect the learning process, we conduct the convergence analysis of DP-OTA-FedAvg in the cases of convex and non-convex loss functions. Based on these analytical results, we formulate an optimization problem to minimize the optimality gap of the DP-OTA-FedAvg subject to limited sum power and privacy budgets. The problem is solved by decoupling it into two sub-problems. Given the number of communication rounds, we conclude the relationship between the number of scheduled devices and the alignment coefficient, which offers a set of potential optimal solution pairs of device scheduling and the alignment coefficient. Thanks to the reduced search space, the optimal solution can be efficiently obtained. The effectiveness of the proposed policy is validated through simulations.

Design of Two-Level Incentive Mechanisms for Hierarchical Federated Learning

Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' models simultaneously. To enable efficient HFL, it is crucial to design suitable incentive mechanisms to ensure that devices actively participate in local training. However, there are few studies on incentive mechanism design for HFL. In this paper, we design two-level incentive mechanisms for the HFL with a two-tiered computing structure to encourage the participation of entities in each tier in the HFL training. In the lower-level game, we propose a coalition formation game to joint optimize the edge association and bandwidth allocation problem, and obtain efficient coalition partitions by the proposed preference rule, which can be proven to be stable by exact potential game. In the upper-level game, we design the Stackelberg game algorithm, which not only determines the optimal number of edge aggregations for edge servers to maximize their utility, but also optimize the unit reward provided for the edge aggregation performance to ensure the interests of cloud servers. Furthermore, numerical results indicate that the proposed algorithms can achieve better performance than the benchmark schemes.

Beamforming Design for RIS-Aided AF Relay Networks

Since reconfigurable intelligent surface (RIS) is considered to be a passive reflector for rate performance enhancement, a RIS-aided amplify-and-forward (AF) relay network is presented. By jointly optimizing the beamforming matrix at AF relay and the phase shifts matrices at RIS, two schemes are put forward to address a maximizing signal-to-noise ratio (SNR) problem. Firstly, aiming at achieving a high rate, a high-performance alternating optimization (AO) method based on Charnes-Cooper transformation and semidefinite programming (CCT-SDP) is proposed, where the optimization problem is decomposed to three subproblems solved by CCT-SDP and rank-one solutions can be recovered by Gaussian randomization. While the optimization variables in CCT-SDP method are matrices, which leads to extremely high complexity. In order to reduce the complexity, a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation (DT-SCA) is put forward, where matrices variables are transformed to vector variables and three decoupled subproblems are solved by DT-SCA. Simulation results verify that compared to two benchmarks (i.e. a RIS-assisted AF relay network with random phase and a AF relay network without RIS), the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance. Furthermore, it is revealed that the rate of the low-complexity DT-SCA method is close to that of CCT-SDP method.

Low-Complexity Three-Dimensional AOA-Cross Geometric Center Localization Methods Using Massive MIMO Receive Array

Angle of arrival (AOA) is widely used to locate a wireless signal emitter. Compared with received signal strength (RSS) and time of arrival (TOA), it has higher accuracy and is not sensitive to time synchronization of the distributed sensors. However, there are few works focused on three-dimensional (3-D) scenario. Furthermore, although maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra high. It is hard to employ it in practical applications. This paper proposed two multiplane geometric center based methods for 3-D AOA positioning. The first method could estimate the source position and angle measurement noise at the same time by seeking a center of the inscribed sphere, called CIS. Firstly, every sensor could measure two angles, azimuth angle and elevation angle. Based on that, two planes are constructed. Then, the estimated values of source position and angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramer-Rao lower bound (CRLB) and have lower complexity than MLE.

Beamforming and Phase Shift Design for HR-IRS-aided Directional Modulation Network with a Malicious Attacker

In this paper, we propose to use hybrid relay-intelligent reflecting surface (HR-IRS) to improve the security performance of directional modulation (DM) system. In particular, the eavesdropper in this system works in full-duplex (FD) mode and he will eavesdrop on the confidential message (CM) as well as send malicious jamming. We aim to maximize the secrecy rate (SR) by jointly optimizing the receive beamforming, transmit beamforming and phase shift matrix (PSM) of HR-IRS. Since the optimization problem is un-convex and the variables are coupled to each other, we solve this problem by iteratively optimizing these variables. The receive beamforming and transmit beamforming are obtained based on generalized Rayleigh-Ritz theorem and Dinkelbach's Transform respectively. And for PSM, two methods, called separate optimization of PSM (SO-PSM) and joint optimization of PSM (JO-PSM) are proposed. Thus, two iterative algorithms are proposed accordingly, namely maximizing SR based on SO-PSM (Max-SR-SOP) and maximizing SR based on JO-PSM (Max-SR-JOP). The former has better performance and the latter has lower complexity. The simulation results show that when HR-IRS has sufficient power budget, the proposed Max-SR-SOP and Max-SR-JOP can enable HR-IRS-aided DM network to obtain higher SR than passive IRS-aided DM network.