This article proposes a novel covert communication framework utilizing movable antennas (MAs) to enable covert communications in which the evading detection eavesdropper aided by a reconfigurable intelligent surface (RIS). The trajectories of the MAs over the entire time slot, transmit beamforming, and the phase shift of the RIS in each time slot are jointly optimized to improve the covert rate. Specifically, the movement trajectories of the MAs are modeled as a Markov decision process (MDP), optimized by developing a novel deep reinforcement learning (DRL) approach. Furthermore, an alternating optimization (AO) algorithm is designed to jointly optimize the beamforming and phase. In particular, penalty-based two-layer iterative algorithm is proposed to guarantee that the solution satisfies the rank-one constraints. Numerical results show that the proposed MA-assisted covert communications system significantly outperforms conventional fixed-position antenna (FPA) schemes in terms of covert rate.

This article explores the benefits of deploying simultaneously transmitting and reflecting reconfigurable intelligence surfaces (STAR-RIS) in autonomous aerial vehicle (AAV) networks with simultaneous wireless information and power transfer. In the proposed system, the AAV utilizes STAR-RIS to radiate energy-carrying information signals (ECISs) to multiple outdoor energy receivers and multiple indoor information receivers without flying over indoor no-fly zone. Based on the AAV propulsion power formula, we successively introduce fly-hover-broadcast (FHB) and path discretization (PD) protocols to minimize the total AAV energy consumption by jointly using the extended penalty function and optimization algorithm based on the expected value of channel status information, where the AAV flight constraints, no-fly constraint, minimized energy or information threshold constraints, and STAR-RIS phase-shift constraints are met. The FHB protocol, which requires the AAV to radiate the ECISs to the users at only a few hovering positions, provides a lower bound performance of AAV energy consumption, while the PD protocol is used to discuss the general situation of the ECISs during AAV flight. Simulation results demonstrate that the utilization of the STAR-RIS in AAV networks outperforms the traditional RIS in improving energy efficiency and extending AAV flight time.

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLCF) is proposed to reduce the feedback and improve the spectral efficiency for FDD massive MIMO. Manifold learning (ML) is an effective method for dimensionality reduction. However, most ML algorithms focus only on data compression, and lack the corresponding recovery methods. Moreover, the computational complexity is high when dealing with incremental data. Considering to utilize the intrinsic manifold structure where the CSI samples reside, we propose a landmark selection algorithm to describe the topological skeleton of this manifold. Based on the learned skeleton, the local patch of the incremental CSI on the manifold can be easily determined by its nearest landmarks. This motivates us to propose an incremental CSI compression and reconstruction scheme by keeping the local geometric relationships with landmarks invariant. We theoretically prove the convergence of the proposed landmark selection algorithm. Meanwhile, the upper bound on the error of approximating CSI with landmarks is derived. Simulation results under an industrial channel model of 3GPP demonstrate that the proposed MLCF outperforms existing deep learning based algorithms.

The accurate prediction of required resources in terms of storage, computing and bandwidth is essential for 5G to host diverse services. The existing efforts illustrate that it is more promising to efficiently predict the unknown required resources with a third-order tensor compared to the 2D-matrix-based solutions. However, most of them fail to leverage the inherent features hidden in network traffic like temporal stability and service correlation to build a third-order tensor for the multi-dimensional required resource prediction in an intelligent manner, incurring coarse-grained prediction accuracy. Furthermore, it is difficult to build a third-order tensor with rate-varied measurements in 5G due to different lengths of measurement time slots. To address these issues, we propose an Accurate Prediction of Multi-Dimensional Required Resources (APMR) approach in 5G via Federated Deep Reinforcement Learning (FDRL). We first confirm the resource requests originated from different Base Stations (BSs) at varied measurement rates have similar features in service and time domains, but cannot directly form a series of regular tensors. Built on these observations, we reshape these measurement data to form a series of standard third-order tensors with the same size, which include many elements obtained from measurements and some unknown elements needed to be inferred. In order to obtain accurately predicted results, the FDRL-based tensor factorization approach is introduced to intelligently utilize multiple specific iteration rules for local model learning, and the accuracy-aware and latency-based depreciation strategies are exploited to aggregate local models for resource prediction. Extensive simulation experiments demonstrate that APMR can accurately predict the multi-dimensional required resources compared to the state-of-the-art approaches.

This study explores the performance optimization of uplink orthogonal frequency division multiple access (OFDMA)-based random access (UORA) in IEEE 802.11ax networks. UORA supports multi-user transmission via two methods, where users transmit either fixed-size or variable-size aggregated MAC protocol data units. However, three critical issues arise. 1 Existing studies only focus on the fixed-size method with low practicality, and overlook the impact of traffic load which leads to inaccurate evaluation of the network performance. 2 The variable-size method has never been studied due to a complex scenario, where user frames append padding bits to fulfill the transmission opportunity constraint. 3 In realistic networks, the variable-size method sacrifices throughput to achieve high practicality and low latency. To address the first two issues, we proposed two novel models based on queueing theory that accurately capture the impact of these transmission methods and various parameters (e.g., the traffic load and padding bits) on throughput, packet loss rate, and latency. To address Issue 3, we design aDynamicSelectionAlgorithm ofTransmissionMethods (DSATM), which dynamically switches between the two transmission methods to enhance practicality, maximize throughput, and minimize latency. Finally, we conducted extensive simulations to verify the accuracy of our models and DSATM.

Trajectory synthesis with a series of fake locations has been deemed as a promising obfuscation technology to preserve the individual privacy of users in Location-Based Services (LBSs). However, a number of previous approaches fail to take into consideration the geographic distance and motion direction of the real locations to synthesize trajectories. As a result, most of them always cannot represent the statistical characteristics of real trajectories in a privacy-preserving manner, and thus suffer from various attacks through data analysis. To tackle this issue, this paper presents SPSD, a novel privacy-preserving trajectory synthesis approach with a$k$-anonymous guarantee, through extracting the semantic, geographic and directional similarity of locations from the real trajectories to create plausible trajectories. SPSD first classifies all historical trajectory data into a series of sets for location identity, by introducing the visiting time and visiting duration, which can clearly represent the semantic information of locations. Then,$4k$locations and$2k$of$4k$ones have been selected from each set to act as the initial disguises of each corresponding real location, with quantitative semantic and geographic similarities, respectively. In order to find enough fake locations for each real location in less time, the candidate locations have been narrowed down to$k$in direction recovery through step-by-step screening, with the$k$-anonymous property. Experiment results built on the real-world trajectory datasets indicate that SPSD has outperformed the previous approaches in terms of semantic similarity, directional accuracy and security resistance to synthesize privacy-preserving trajectories at the tolerable time cost.

Interferences due to non-orthogonality of training sequences usually exist in cellular networks when the number of all users is relatively large compared to the coherence time, such as the case in massive MIMO systems. In this paper, we address this effect from the perspective of non-orthogonal training sequence design in two-cell interference networks with K users per cell. We relax the general assumption in which the cross-correlations of sequences are restricted to be 0 or 1, and target at designing the training sequences to minimize training phase interference with a given pilot length τ, which is no larger than the total number of users, i.e., τ ∈ [K, 2K]. We note that when large scale fading between different cells β ≠ 1, the strengths of interferences arising from non-orthogonal training sequences within a cell or from the adjacent cell become asymmetric, and optimal design needs to treat the intra-cell sequence correlation and inter-cell correlation differently. To this end, by incorporating β into the design, we extend the Welch bound (Welch 1974 [1]) to the two-cell scenario with asymmetric intra-cell and inter-cell interference, and characterize the lower bound of the interference precisely. Specifically, we obtain the result that the sum of the squares of β -weighted cross-correlations of the training sequences is lower-bounded by [(2K2(1+β2))/(K+(τ-K)β2)], which can be achieved by the proposed training sequence design in closed-form. Particularly, when β = 1, this bound reduces to [((2K)2)/(τ)] which is exactly the Welch bound. This result is applicable for the uplink design of general interference networks such as the pilot design in massive MIMO and the signature sequence design in multicell CDMA systems.

In this paper, we aim to maximize the total energy efficiency for a multi-cell MIMO broadcast channel with dirty paper coding, where both the base stations and users employ multiple antennas. The EE metric is defined as the ratio of the total sum- rate to the total power consumption. Because the original problem is non-convex and difficult to tackle directly, we employ the fractional programming and iterative linear approximation methods to transform it into a set of sub-problems. After using Lagrange dual decomposition, each sub- problem essentially becomes a precoding design problem in MIMO broadcast channel (BC) and is still non-convex, which is then dealt with via BC- multiple access channel (MAC) duality property. Specifically, we propose a gradient descent (GD) method to compute the uplink precoder in each MAC problem which has low complexity. Thus, the dual BC problem can be solved and each BS can iteratively update its precoding matrices with small amount of information exchange among the base stations. Numerical results validate the better performance of our proposed algorithm over conventional linear precoding method.

In this study, the authors propose a low complexity detector for the generalised space shift keying (GSSK) in large‐scale multiple‐input–multiple‐output systems. To be concrete, they propose a sparse K ‐best (SK) detector based on the breadth‐first category of sphere detector (referred to K ‐best sphere decoding). The author's detector is inspired by the fact that the GSSK signal is naturally a sparse zero‐one vector since only a few antennas are activated at the transmitter. Different with the conventional K ‐best detector searching all the transmit antennas, their proposed SK detector investigates only a few promising candidates which are activated antennas at the transmitter. Overall, their proposed SK detector exploits not only the sparsity of the GSSK signal but also the constraint on its non‐zero values. Therefore the restricted isometry property‐based performance analysis shows that is effective in detecting the GSSK signal. Moreover, the empirical results show that their detector performs much better than the sparse algorithms‐based normalised compressive sensing (NCS) detectors while exhibits only slightly higher complexity than the latter (the low‐complexity orthogonal matching pursuit‐based NCS detector).

Equispaced pilot arrangement is the most popular scheme for pilot‐aided transmission in orthogonal frequency division multiplexing systems. In this study, the authors argue that a non‐equispaced pilot pattern may outperform its equispaced counterpart, if they fully take into account the sparsity of the channel impulse response which is inherent in wireless channels. More specifically, a sparsity‐aware pilot arrangement scheme based on the coherence criterion is investigated in this study. To address the resulting non‐deterministic polynomial (NP)‐hard combinatorial optimisation problem, they propose an efficient local search algorithm. For channel estimation, they convert it to a sparse recovery problem. To enhance the applicability of the authors scheme, that is, when there is no prior knowledge about the channel order, they propose to employ the Bayesian information criterion to estimate the channel order first and then recover the sparse channel vector via existing low‐complexity methods, for example, orthogonal matching pursuit. By combining the above pilot arrangement scheme with channel estimation, their scheme exhibits substantially better performance in comparison with the conventional equispaced schemes with linear (or spline) interpolation, in terms of total number of pilot symbols and bit error rate.

In wireless networks, bandwidth is extremely valuable resource. Therefore, an effective call admission control is urgent for bandwidth allocation with the occurrence of handoff increasing. The main challenges with multiple types of traffic are that each has its own requirements of bandwidth, traffic characteristic, QoS guarantee and handoff rate. Moreover, the computational complexity is another challenge. To meet these challenges, we set up a fictitious stochastic model to study the actual system so as to avoid coping with the complex multiple dimensions stochastic problem. On one hand, it can turn the multiple steps of state transition into single step, which is a necessary condition for ideal birth-death processes. On the other hand, it can provide a simple and effective method to compute a little larger approximation of the call dropping probabilities for multiple services, which facilitate our estimation for the acceptance ratio vector subject to QoS requirement. In addition, the boundary conditions are also considered in order to improve the control precision. Furthermore, a fast numerical method has been found to decrease the computation complexity greatly. As a result, we get an effective multiservices dynamic call admission scheme called EMDCA to adapt for multiple types of services in broadband wireless networks. Numerical results of simulation show that our scheme steadily satisfies the hard constraint on call dropping probability of multiservices while maintaining a high channel throughput.

One of the key techniques in ensuring the quality of service in wireless networks is call admission control. The traditional well-known guard channel (GC) scheme and its numerous variants give preferential treatment to the handoff calls over new arrivals by reserving a number of radio channels exclusively for handoffs. However, this scheme is not able to adapt to changes in traffic pattern due to the static nature. The SDCA (stable dynamic call admission) control mechanism proposed by S. Wu (2002) can overcome that shortcoming, and improve the control precision and stability greatly because of its stochastic nature. Unfortunately, it is only suitable for single-service. In this paper, call admission control mechanism is extended by setting up a fictitious stochastic model to study the actual system so as to void meeting the complicated multiple dimensions stochastic problem and propose a new scheme called multiservices dynamic call admission (MDCA) to adapt for multiservices in the broadband wireless networks. Numerical results of simulation show that our scheme steadily satisfies the hard constraint on call dropping probability of multiservices while maintaining a high channel throughput.