This paper explores the application of movable antenna (MA), a cutting-edge technology with the capability of altering antenna positions, in a symbiotic radio (SR) system enabled by reconfigurable intelligent surface (RIS). The goal is to fully exploit the capabilities of both MA and RIS, constructing a better transmission environment for the co-existing primary and secondary transmission systems. For both parasitic SR (PSR) and commensal SR (CSR) scenarios with the channel uncertainties experienced by all transmission links, we design a robust transmission scheme with the goal of maximizing the primary rate while ensuring the secondary transmission quality. To address the maximization problem with thorny non-convex characteristics, we propose an alternating optimization framework that utilizes the general S-procedure, general sign-definiteness, successive convex approximation (SCA), and simulated annealing (SA) improved particle swarm optimization (SA-PSO) algorithms. Numerical results validate that the CSR scenario significantly outperforms the PSR scenario in terms of primary rate, and also show that compared to the fixed-position antenna scheme, the proposed MA scheme can increase the primary rate by 1.48 bps/Hz and 1.57 bps/Hz for the PSR and CSR scenarios, respectively.

Aiming to circumvent the low energy efficiency (EE) dilemma of multiple-input multiple-output (MIMO) systems induced by employing hundreds of antennas, this paper investigates the potentials of stacked metasurface (SM) and semantic communications (SemCom) for achieving energy-efficient holographic communications in MIMO systems. Specifically, SM enables hybrid beamforming with increased degrees of freedom (DoFs) and reduced energy consumption, while SemCom transmits dramatically compressed key informantion that comes with low power consumption and high EE. To this end, we formulate a worstcase semantic EE (Sem-EE) maximization problem in terms of the transmit beamformer and SM's phase shifts. By proposing a semantic majorization-minimization to handle the fractional and quasi-convex Sem-EE form, quadratically constrained quadratic programs and cyclic coordinate descent can be exploited to solve the optimization variables with low computational complexity. Numerical simulations demonstrate the enhanced EE performance of SMaided semantic beamforming scheme compared to the conventional MIMO systems.

This article investigates the dynamic frequency selection problem in broadband anti-jamming communications through distributed sensing and deep reinforcement learning (DRL). In broadband anti-jamming scenarios, a single agent is often impractical for sensing the whole range of the band due to various restrictions on implementation. In this article, a novel distributed sensing architecture is proposed, where a number of distributed sensors are cooperated for sensing the whole band with each sensor only responsible for an individual sub-band. In this way, a fusion center should collect the sensing spectrum from each sub-band and the problem of spectrum compression is formulated under the framework of autoencoder. To cope with the problem of unavailable reward in distributed sensing, we propose a domain-adapted reward estimation method, with which the agent could work near perfectly under the framework of DRL. Simulation results show that the distributed sensing method could work effectively, and the compression ratio of the proposed autoencoder-based method could be 14 times higher than that of the direct quantization approach without any degradation on the final anti-jamming performance. In addition, the proposed DRL-based anti-jamming agent using reconstructed spectrum waterfall and estimated rewards achieves close-to-ideal performance in the environment with unknown jamming patterns.

Due to the users' overlapping channels and the open nature of the wireless medium, inter-user interference and malicious jamming attacks deteriorate the performance of unmanned aerial vehicle (UAV) communications. With this focus, this paper proposes a novel integration of dual polarization, rate-splitting multiple access (RSMA), and stacked reconfigurable intelligent surface (RIS) transceiver into UAV networks, thus simultaneously mitigating the inter-user interference and malicious interference by fully exploiting their potentials in the power, space, and polarization domains. Building upon this architectural framework, a generalized sum rate maximization problem is formulated under the jammer's imperfect angular channel state information and unknown cross-polarization discrimination. To efficiently tackle the challenges posed by the intractable non-convex design problem with both high-dimensional variables and the multiple QoS constraints, a low-complexity optimization framework is presented, where a discretization method combined with quadratic property, a reduced-majorization-minimization algorithm, and two computationally efficient algorithms using block successive upper-bound minimization are developed to obtain the semi-closed-form solutions. Finally, numerical simulations verify the superiority and validity of our proposed architecture and optimization framework over benchmarks.

Semantic communication is a promising approach to address the challenge of limited spectrum resources in the sixth-generation (6G) communication networks. However, prior works on semantic communication focus primarily on semantic coding, and they do not investigate how to efficiently construct a semantic knowledge base. In this paper, a codebook-based unified hierarchical semantic knowledge base (UH-SKB) framework is studied for multi-task semantic communications. To maximize semantic representation spaces and effectively explore the semantic relevance among multiple tasks, the semantic knowledge base is constructed jointly in both the horizontal and vertical directions. A deep K-subspace cluster method is proposed to facilitate semantic relevance extraction and semantic subspace construction for high-dimensional semantic information. Simulation results demonstrate that the proposed UH-SKB can support multi-task semantic communications efficiently, achieving up to 13.4%, 14% and 6.3% performance improvement respectively for reconstruction, segmentation and classification tasks compared to standalone semantic knowledge bases at the novel dataset when SNR is 0 dB. Moreover, the proposed UH-SKB exhibits 95.3% knowledge search efficiency improvement on the reconstruction task compared to standalone semantic knowledge bases.

In this paper, we propose a movable antenna (MA) empowered scheme for symbiotic radio (SR) communication systems. Specifically, multiple antennas at the primary transmitter (PT) can be flexibly moved to favorable locations to boost the channel conditions of the primary and secondary transmissions. The primary transmission is achieved by the active transmission from the PT to the primary user (PU), while the backscatter device (BD) takes a ride over the incident signal from the PT to passively send the secondary signal to the PU. Under this setup, we consider a primary rate maximization problem by jointly optimizing the transmit beamforming and the positions of MAs at the PT under a practical bit error rate constraint on the secondary transmission. Then, an alternating optimization framework with the utilization of the successive convex approximation, semi-definite processing and simulated annealing (SA) modified particle swarm optimization (SA-PSO) methods is proposed to find the solution of the transmit beamforming and MAs' positions. Finally, numerical results are provided to demonstrate the performance improvement provided by the proposed MA empowered scheme and the proposed algorithm.

By decoupling the dedicated radio frequency (RF) chain into transmit RF (TX RF) chain and receive RF (RX RF) chain, the asymmetrical system can flexibly equip the downlink/uplink array with different number of TX/RX RF chain according to the practical demand in a massive multiple-input multiple-output Internet of Things (IoT) network. To reduce cost and power consumption, this paper maximizes the uplink resource efficiency (RE) under Weichselberger channel model by designing transmit covariance matrices and receive antenna selection (RAS). In IoT networks with multiple IoT nodes, we propose an alternate optimization algorithm to iteratively optimize transmit covariance matrices and RAS by exploiting statistical channel state information. Specifically, for correlated channels, we propose a penalty method-based algorithm for RAS which utilizes Dinkelbach’s transform and linear relaxation to tackle the intractable fractional function and binary constrain, respectively. Compared with greedy search, the proposed algorithm has lower complexity without much loss of performance. For independent identically distributed channels, we simplify the RE maximization problem and provide the necessary conditions of the optimal number of receive antennas and transmit power. Finally, the validness of our conclusions as well as the effectiveness of proposed algorithms are illustrated by numerical simulations.

Most existing deep learning (DL) based direction-of-arrival (DOA) estimation methods treat direction finding problem as a multi-label classification task and the output of the neural network is a probability spectrum where the peaks indicate the true DOAs. These methods essentially belong to grid-based methods and may encounter grid mismatch effect. In this paper, we focus on gridless DL based DOA estimation under generalized linear array which can be regarded as a uniform linear array (ULA) with/without “holes”. By using the Toeplitz structure, a deep convolutional neural network (CNN) is proposed to estimate the noiseless covariance matrix of the aforementioned ULA with “no holes,” based on which the DOAs can be retrieved by using root-MUSIC. To increase the generalization, the parameters of the CNN with respect to different number of sources are pre-trained and stored in a database. We then propose another CNN for source enumeration in order to choose suitable parameters from the database. Our method can find more sources than sensors and do not suffer from the grid mismatch effect.

In this paper, we focus on the energy saving problem for the wireless sensor networks (WSNs). Specifically, we propose a UAV-assisted wireless network architecture, where the cell-edge sensor devices (SDs) can access the aerial access points (AAPs) instead of the terrestrial access point (TAP). Since the transmitter-to-receiver distance is shortened and the ground-to-air channel is usually line-of-sight, the SDs can use lower power to transmit data and thereby prolong their lifetime. To fully exploit the potential of the network architecture, we jointly optimize the AAPs' position, channel allocation, and power control to minimize the total transmission power of all SDs. In order to solve the complex joint optimization problem, we reformulate it as three tractable subproblems and use the methods in graph theory to design low-complex algorithms. Simulation results indicate that the proposed network architecture greatly outperforms the traditional WSNs, and the proposed algorithms can further reduce the total power consumption.

Cooperative communications (CC) based on network coding (NC) can significantly increase the capacity of wireless networks. In this paper, we investigate how to apply NC in multi-hop wireless networks with multiple relay nodes and multiple sessions to achieve more practical and general CC based on NC. Based on NC, we consider to jointly optimize relay node selecting, scheduling and flow routing in CC in a multi-hop wireless network. We not only construct a mathematical model but also formulate a maximum-minimum (Max-Min) optimization problem for it. Then we reformulate and solve it with CPLEX. Through simulation, we validate that the achieved rate gain by NC can be greatly improved than the one by CC.

In the foreseeable future, data transmission and information exchange play important roles in intelligent transportation (ITS) systems. Heterogeneous networks (HetNets) currently emerge in order to provide large amount of data flow and customized network services. Virtualization technology is considered as one most promising approach towards HetNets implementation, aiming at managing virtualized HetNets resources in a convenient manner. In the virtualization research, the virtual resource allocation is another core technical issue. Though the issue has been well addressed in recent years, the secure virtual resource allocation has not been fully investigated yet. Hence, we research the secure virtual resource allocation for HetNets in this paper. Following the introduction of the security model for HetNets, we propose one effective heuristic strategy, incorporating the greedy and isolation methods. Experiment work is conducted so as to validate the efficiency of the heuristic strategy. Experiment results vividly reveal that our heuristic strategy, incorporating the greedy and isolation methods, performs better than the counterpart without using the isolation method, in terms of average virtual network service acceptance.

In this paper, we propose a relay cooperation scheme for backscatter communication systems for performance enhancement, in which one user backscatters incident signals from a power beacon (PB) to a relay and a receiver simultaneously, and then the relay decodes the received signals and forwards the decoded signals to the receiver. We consider two cases that the relay is with/without an embedded energy source. In particular, if the relay does not have an energy source, an energy harvesting phase is required, during which the relay harvests energy from the PB while the user backscatters information to the receiver. We first formulate system throughput maximization problems for both cases by finding the optimal time allocation schemes, from which some useful insights are provided. Then, with a given amount of information required to be delivered, the transmission time minimization problems for both cases are also formulated, and the optimal solutions are derived in closed-form. Numerical results reveal the proposed scheme can significantly enhance the system throughput and transmission time.

Group paging is a baseline solution proposed by the long-term evolution (LTE) standardization body for supporting machine-to-machine (M2M) communications in the current-generation and the next-generation cellular networks. Yet, in conventional group paging scheme, upon the reception of paging message, all machine-type devices (MTDs) in a group will simultaneously access the base station, leading to severe network congestion and intolerably low access efficiency. To handle this issue, in this article, we propose a dynamic group paging mechanism, where only the MTDs with packets to send will join the contention process, and the collisions in the random access channel are addressed by the contention resolution scheme. Explicit expressions of key performance measures including the mean access delay of each MTD are derived as functions of the length of waiting period (interval between two consecutive paging periods) TW, where a smaller TWindicates a higher frequency of group paging. It is shown that TWis a key system parameter that determines the crucial tradeoff between the signaling overheads of the system during the paging period and the access delay performance of each MTD. To study how to properly tune the waiting period length, a utility-based analytical framework is established by taking the aforementioned tradeoff into consideration. The optimal waiting period length for maximizing the network utility is derived and verified by simulation results. The analysis in this article reveals that the network should increase the group paging frequency as the traffic becomes heavier or the number of preambles decreases. Providing more preambles can indeed improve the delay performance, while the gain becomes marginal if the number of preambles is large.

Cooperation between primary and secondary networks offers significant benefits in data forwarding. But, cost implication in such cooperation is not well understood. In this paper, we explore cost incurred in both primary and secondary networks when they are allowed to relay each other's traffic in a cooperative manner. We model costs in both networks and formulate a multiobjective optimization problem. For this problem, we present a novel algorithm to construct an ε-approximation curve and prove its error bounds in both cost dimensions. Based on the ε-approximation curve, we develop three important applications. The first application is to show the minimum cost value for a single objective (by fixing the other objective as constant) or the relationship between the two objectives over the entire range of possible values. The second application is to address different cost parameters in the primary and secondary networks. We show how to obtain a new approximation curve by scaling the original ε-approximation curve with appropriate factors and quantify its error bounds. The third application is to use the ε-approximation curve to study a single objective optimization problem with a guaranteed error bound. The results in this paper offer some deep theoretical insights on potential costs incurred in both networks when they are allowed to relay each other's traffic cooperatively.

Millimeter-wave communication in 60 GHz band is considered a promising technology to meet the explosive growth of data demand in Wi-Fi based WLAN. To address potential blockage for 60 GHz signals, multiple APs are proposed for such WLAN. This paper addresses the important problem of AP assignment and transmission scheduling for a multi-AP 60 GHz WLAN. We propose two AP assignment schemes with different complexity and study how to maximize user throughput with joint consideration of AP assignment and transmission scheduling. We advocate to use one-shot AP assignment-based scheduling due to its simplicity for implementation. To address real-time online traffic and human blockage, we propose an online algorithm to implement the one-shot AP assignment scheme without altering the AP assignment for other existing users. Through performance evaluation, we show that the proposed online algorithm is competitive when compared to the offline algorithm.

In recent years, there has been great interest from the cellular service providers to use the unlicensed spectrum for their service offerings. On the other hand, existing unlicensed users in these bands (e.g., Wi-Fi in the 5-GHz band) have serious concern that such coexistence will jeopardize their service quality. Although there are some proposals on how to achieve coexistence, they are driven by the service providers and as such there remain many issues and skepticism. In this paper, we take a novel human-centric approach to understand coexistence between Wi-Fi and LTE by focusing on human satisfaction. Through mathematical modeling, problem formulation, and extensive simulations studies, we show that in terms of maximizing total human satisfaction function, there does not appear to be any advantage with the coexistence of unlicensed spectrum for Wi-Fi and LTE under static partitioning of unlicensed spectrum. This finding serves as a powerful counter argument to some LTE service providers' proposal to share the unlicensed spectrum with Wi-Fi through static partitioning. On the other hand, we find that there is a significant improvement in human satisfaction in coexistence between Wi-Fi and LTE under adaptive spectrum partitioning. Since adaptive spectrum partitioning may require a user to change its service provider whenever there is a change among the users, we propose a practical (semi-adaptive) algorithm for implementation without affecting existing users' service providers. Through performance evaluation, we show that the proposed semi-adaptive algorithm is highly competitive.

Cooperation has become an essential element in spectrum sharing between the primary and secondary networks. A new trend in cooperation is to allow the primary and secondary networks to cooperate on the node level for data forwarding. This new paradigm allows to pool network resources from both the primary and secondary networks and allows users in each network to access a much richer network infrastructure in a combined network. This paper offers an in-depth study of such node-level cooperation by explaining its optimal throughput curve—the maximum achievable throughput for both the primary and secondary users. We formulate the problem as a multicriteria optimization problem with the goal of maximizing the throughput of both the primary and secondary users. Through a novel approach based on weighted Chebyshev norm, we transform the multicriteria optimization problem into a single criteria optimization problem and find a sequence of Pareto-optimal points iteratively. Based on the Pareto-optimal points, we construct the throughput curve and show that it provides an $\varepsilon $ -approximation to the optimal curve. We prove some important properties of the optimal throughput curve. Through a case study, we show that the throughput region (the area under the throughput curve) under node-level cooperation is substantially larger than that when there is no node-level cooperation.

The transparent coexistence (TC) paradigm allows simultaneous activation of the secondary users with the primary users as long as their interference to the primary users can be properly canceled. This paradigm has the potential to offer much more efficient spectrum sharing than the traditional interweave paradigm. In this paper, we design a distributed algorithm to achieve this paradigm for a secondary multi-hop network. For interference cancelation (IC), we employ MIMO at secondary nodes. We present a distributed iterative algorithm to maximize each secondary session's throughput while meeting all IC requirements under TC. By maintaining two local sets for each node, we can keep track of the node's IC responsibility. Although no explicit node ordering is maintained in our distributed algorithm, we prove that our distributed data structure at each node (with the use of two local sets) can be mapped to an explicit global node ordering for IC among all nodes in the network. This guarantees that each active node's degree-of-freedoms allocated for IC is feasible at the physical layer. Our algorithm is iterative in nature and all steps can be accomplished based on local information exchange among the neighboring nodes. We present the simulation results to show that the performance of our distributed algorithm is highly competitive when compared with an upper bound solution from the corresponding centralized problem.