This study addresses the challenge of achieving high-precision indoor positioning in non-line-of-sight (NLoS) environments through the development of an innovative visible light positioning (VLP) system that utilizes optical intelligent reflecting surfaces (OIRS). Unlike current hybrid methodologies that combine both line-of-sight (LoS) and NLoS techniques tailored for Internet of Things (IoT) environments, our novel single-LED architecture relies solely on signals reflected by an OIRS to facilitate accurate positioning in intricate indoor settings where direct light paths are often obstructed. This system employs a two-stage maximum likelihood estimation framework that effectively integrates received signal strength (RSS) and time-of-arrival (ToA) characteristics, thereby addressing the shortcomings of traditional single-feature methods and ensuring reliable performance in densely populated IoT scenarios. To tackle the non-convex optimization problem, we propose an improved grey wolf optimization (IGWO) algorithm, which exhibits superior positioning accuracy and convergence properties when compared to particle swarm optimization and genetic algorithms. Simulation results substantiate the framework’s efficacy, demonstrating improved positioning accuracy. The proposed system presents a cost-effective solution for complex indoor environments where direct light paths are frequently obstructed, thereby advancing the practical application of VLP technologies.

Affine frequency division multiplexing (AFDM) has emerged as a robust multi-carrier modulation candidate for high-mobility communications. This paper investigates an AFDM based integrated sensing and communications (ISAC) framework for unmanned aerial vehicle (UAV) links within space-air-ground integrated networks (SAGINs). A key contribution of this work is the novel design of the cyclic prefix and postfix (CPP) for AFDM, which is specifically tailored to accommodate wireless power transfer (WPT) requirements, thereby supporting simultaneous information and energy transmission. Specifically, the base station exploits the reflected echoes of AFDM signals to estimate sensing parameters, including the position, velocity, and angle of mobile users. To optimize the communication link, we propose an intelligent adaptive modulation and coding (AMC) decision-making process. A specialized dataset is established, integrating physically interpretable metrics—such as distance, velocity, and angle—with historical AFDM channel state information characterized by its unique chirp-domain representation. Subsequently, a hybrid deep learning architecture, designated as CNN-LSTM, is developed to establish a unified evaluation framework. This framework leverages the feature extraction capabilities of convolutional neural networks (CNNs) to process the spatial-temporal correlations of the AFDM channel, while utilizing Long Short-Term Memory (LSTM) networks to capture the long-term temporal dependencies of UAV trajectories. Simulation results demonstrate that the proposed modeling approach achieves superior separability and robustness, aligning closely with the ideal adaptive envelope while exhibiting enhanced cross-trajectory generalization capabilities compared to conventional methodologies.

This paper proposes a robust received signal strength (RSS)-based localization framework for non-cooperative unmanned aerial vehicles. Conventional RSS methods face three fundamental obstacles: susceptibility to heavy-tailed measurement noise, intractable non-convexity, and severe accuracy degradation when target transmission power is unknown. These vulnerabilities present critical security risks to emerging low-altitude economy networks. To overcome these limitations, we propose an integrated joint-estimation architecture. First, a cascaded preprocessing pipeline, combining Gaussian outlier suppression and statistical median weighting, is developed to mitigate multipath-induced biases and minimize variance. Second, an information-theoretic base station (BS) selection mechanism is designed to identify geometrically optimal BSs, thereby exponentially reducing computational overhead in both uniform and random deployment scenarios. Third, the power-unknown problem is reformulated via semidefinite programming, absorbing the unknown parameter into a higher-dimensional convex cone to guarantee global convergence without relying on initial guesses. Extensive Monte Carlo simulations demonstrate that under uniform BS deployment, our strategy achieves sub-10-meter accuracy (approximately 5 m root mean square error) using only 5 selected BSs in typical urban conditions with a path loss exponent of 3. Consequently, this approach delivers a highly accurate and computationally efficient solution for real-time target tracking in complex environments.

Semantic communication, shifting from accurate bitlevel signal delivery to the semantic meaning convey, is an emerging paradigm that has been regarded as a breakthrough beyond the Shannon boundary. In this paper, an unmanned aerial vehicle (UAV)-assisted semantic communication system is considered for data collection, where multiple UAVs are dispatched to gather the observed data generated by ground users (GUs). To combat the possible cross interference of GUs and wireless fading, GUs can convey relevant semantic meaning rather than bit-level raw data, thus the semantic representation, UAVs' service association with GUs, GUs' transmission power as well as UAVs' trajectory should be properly scheduled to maximize semantic spectrum efficiency (S-SE) while completing the semantic data collection within the limited flight time. By leveraging the powerful non-linear approximation nature, semantic meaning generally can be extracted by encoding and decoding neural networks. However, this results in a lack of closed-formed expression for semantic analysis. Therefore, the paper proposes a matching combined with successive convex approximation (SCA) and multi-agent reinforcement learning approach (MADRL) to maximize the S-SE. Numerical simulation results reveal the effectiveness and superiority of the proposed scheme over conventional bit communication in terms of S-SE and energy consumption.

Autonomous aerial vehicle (AAV)-enabled Internet of Things (IoT) exhibits great application potential with its wide coverage, flexible network topology, and diversified services. However, ensuring communication security and efficient spectrum resource utilization in multiuser access scenarios is challenging, given the open nature of AAV channels and the proliferation of communication devices in IoT. To address the above challenges, this article proposes a novel reconfigurable intelligent surface (RIS)-aided AAV collaborative communication framework, where RIS-equipped AAV flexibly serves multiple users. In this work, a rate splitting multiple access (RSMA)-based secure transmission scheme is proposed, where the split public information serves both as useful signals and noise to disrupt eavesdropping. For the proposed scheme, a sum secrecy rate maximization problem is formulated and solved by optimally deploying the AAV’s location, designing the RIS’s phase shift, and power allocation. For this nonconvex problem with a couple of variables, we decompose it and form three separate subissues. Specifically, leveraging the successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques, we first exploit an iterative algorithm for optimizing beamforming vectors and phase-shift matrix of RIS, and the optimal position of the AAV is obtained according to the deep deterministic policy gradient (DDPG). Then, we design an alternating optimization (AO) framework for joint solving. Finally, simulation results validate the efficacy of the proposed scheme in enhancing security, e.g., relative to the nonorthogonal multiple access (NOMA) scheme and benchmark scheme, the secrecy rate of the proposed scheme increased by 29.7% and 71.9%, respectively.

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS’s phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV’s trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.

As a promising emerging technology, the combination of an integrated sensing and communications (ISAC) system and intelligent reflecting surface (IRS) can obtain better comprehensive properties for both communication and target sensing. To explore the potential ability of a multi-IRS assisted ISAC system, both the active beamforming matrix and passive beamforming matrix should be delicately jointly optimized for simultaneous communication and sensing enhancement, which introduce additional hardware complexity and difficulty to obtain a solution by the conventional methods. Therefore, this paper proposes a successive convex approximation (SCA) and multi-agent deep reinforcement learning (MADRL) combined approach to enhance the communication capability with the target sensing beampattern constraints. In particular, numerical results show that the sensing performance can be guaranteed with the boosting of communication capability, especially when the users are not directly visible to the BS.

With the emergence of the fifth generation (5G) era, the development of the Internet of Things (IoT) network has been accelerated with a new impetus, making it imperative to strive for a more reliable and efficient network environment. To accomplish this, we introduce and investigate a novel proposal for the intelligent reflecting surface (IRS) enabled uplink secure receive spatial modulation (SM), named IRS-USRSM, to resolve the security issues arising from the open wireless transmission environment in the 5G IoT network. In the IRS-USRSM scheme, we assume that the passive eavesdropper is directly connected to the uplink user and occasionally connected to the IRS. To achieve enhanced secrecy with finite alphabet inputs, a joint transmitter perturbation and IRS reflection design for physical layer security is proposed to guarantee secure and reliable transmission of IRS-USRSM. Specifically, two categories of IRS-based random phase compensation strategies, namely, random perturbation compensation and random path synthesize, along with maximum likelihood detection and suboptimal detection are proposed to meet the variant design requirements between achieved performance and system cost. Furthermore, in order to evaluate the performance limits of the IRS-USRSM, the closed-form results of average bit error probabilities and discrete-input continuous-output memoryless channel capacities are derived using the method of moment generating function. Simulation results are presented to verify the correctness of our theoretical analyses, as well as to demonstrate the efficiency and superiority of the proposed IRS-USRSM scheme.

The emerging drone swarms are capable of carrying out sophisticated tasks in support of demanding Internet-of-Things (IoT) applications by synergistically working together. However, the target area may be out of the coverage of the ground station and it may be impractical to deploy a large number of drones in the target area due to cost, electromagnetic interference and flight-safety regulations. By exploiting the innate agility and mobility of unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone swarm network architecture, which is capable of extending the coverage of the ground station and enhancing the effective end-to-end throughput. Explicitly, a swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing and collecting data with the aid of their mounted cameras and/or sensors, and a powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the DCDS and a ground station (GS). Given a time period, in order to maximize the data delivered whilst minimizing the delay imposed, we harness an -multiple objective genetic algorithm (-MOGA) assisted Pareto-optimization scheme. Our simulation results demonstrate that the proposed mobile relaying is capable of delivering more data. As specific examples investigated in our simulations, our mobile relaying-assisted drone swarm network is capable of delivering 45.38% more data than the benchmark solutions, when a stationary relay is available, and it is capable of delivering 26.86% more data than the benchmark solutions when no stationary relay is available.

As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space–air–ground-integrated network (SAGIN) with diverse resource constraints. In this article, we dynamically consider three typical classes of radio access network (RAN) slices, namely, high-throughput slices, low-delay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay, and the coverage area of these three classes of RAN slices are jointly optimized in a nonscalar form by considering the distinct channel features and service advantages of the terrestrial, aerial, and satellite components of acrshortpl SAGIN. A joint central and distributed multiagent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto-optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the interslice subchannel and power sharing by relying on a centralized unit. Then, it optimizes the intraslice subchannel and power allocation, and the virtual base station (vBS)/vUAV/virtual low Earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.

Due to the high flexibility and mobility, unmanned aerial vehicles (UAVs) can be deployed as aerial relays touring to serve ground users (GUs), especially when the ground base station is temporally damaged. However, the broadcasting nature of wireless channels makes such communication vulnerable to be wiretapped by malicious eavesdropping users (EUs). Besides the collecting offloading data for legitimate GUs, UAVs are also expected to be friendly jammers, i.e., generating artificial noise (AN) to deteriorate the wiretapping of EUs. With this in mind, a novel role switching scheme (RSS) is proposed in the paper to guarantee the secure communication by the cooperation of multiple UAVs, where each UAV is allowed to switch its role as a collector or a jammer autonomously to explore a wider trajectory space. It’s worthy to be noticed that the joint optimization for the trajectory of UAVs and the transmission power of GUs and UAVs with role switching scheme is a non-convex mixed integer non-linear programming (MINLP) problem. Since the relaxation of binary variables will lead the solution dropping into local minimum, a deep reinforcement learning (DRL) combined successive convex approximate (SCA) algorithm is further designed to maximize the achievable secrecy rate (ASR) of GUs. Numerical results illustrate that compared with the role fixed scheme (RFS) and relaxation based SCA approaches, the proposed DRL-SCA algorithm endows UAVs the capacity to fly close enough to target users (both GUs and EUs) with less moving distance which brings better ASR and less energy consumption.

Symbiotic radio (SR) is a key technique to solve the energy shortage and spectrum limitation of the future Internet of Things (IoT). In the SR-aided IoT networks supporting energy harvesting (EH), we study the cooperation schemes and offloading strategy between the primary users (PUs), IoT devices, and the base station (BS) for reasonably allocating the spectrum, power, and time resources. Considering the monetary transactions between the PUs and IoT devices, two cooperation schemes, namely, the “Preferential Scenario” and the “No-Preferential Scenario,” are proposed. In the Preferential Scenario, based on the final strategy, the IoT devices use the purchased spectrum and power to offload their own tasks to the BS after assisting the cooperative PUs to offload during a certain time slot. Due to the assistance of IoT devices for the PUs, IoT devices enjoy a discount when paying for the purchased spectrum and power. In the No-Preferential Scenario, the IoT devices and the cooperative PUs offload tasks to the BS together in a certain time slot according to the offloading strategy. The spectrum and power used by the IoT devices are purchased at the original price without a discount. For each scenario, we study the utility maximization problem of the PUs, where the utility of PUs includes the transmission rates and income. The utility-based resource-sharing algorithm is proposed to obtain an approximately optimal resource allocation scheme. Our simulation results indicate that the proposed algorithm provides good performances for both scenarios, while each scenario applying the proposed algorithm has its own advantages.

Low altitude platform (LAP) unmanned aerial vehicles (UAVs), also called drones, are currently being exploited by Edge computing (EC) systems to execute complex resource-hungry use cases, such as virtual reality, smart cities, autonomous vehicles, etc., by attaching portable edge devices on them. However, a typical drone has limited flight time, coupled with the resource-constrained attached edge device, which can jeopardize aerial computing missions if they are not holistically taking into consideration. Moreover, the fundamental challenge is how to co-schedule multi-drone among multi-location where EC services are needed, such that drones are scheduled to maximize the utility from the activities while meeting computing resource and flight time constraints. Therefore, for a given fleet of drones and tasks across disjointed target locations in a city, we derive a machine learning (ML) linear regression model that estimates these tasks resource requirement and execution time. Leveraging this estimation values, we jointly consider each drone’s flight time availability and its attached edge device resource capacity, and formulate a novel Multi-Location Capacitated Mission Scheduling Problem (MLCMSP) that selects suitable drones and co-schedules their flight routes with the least total distance to visit and execute tasks at the target locations. Then, we show that faster scheduling and execution of complex tasks at each location, while considering the inter-task dependencies is important to achieve effective solution for our MLCMSP. Hence, we further propose EdgeDrones, a variant bin-packing optimization approach through gang-scheduling of inter-dependent tasks that co-schedules and co-locates tasks tightly so as to achieve faster execution time, as well as to fully utilize available resources. Extensive experiments on Alibaba cluster trace with information on task dependencies (about 12,207,703 dependencies) show that EdgeDrones achieves up to 73% higher resource utilization, up to 17.6 times faster executions, and up to 2.87 times faster flight travel time compared to the baseline approaches.

Internet of Vehicles (IoV) enables a wealth of modern vehicular applications, such as pedestrian detection, real-time video analytics, etc., that can help to improve traffic efficiency and driving safety. However, these applications impose significant resource demands on the in-vehicle resource-constrained Edge Computing (EC) device installation. In this article, we study the problem of resource-aware offloading of these computation-intensive applications to the Closest roadside units (RSUs) or telecommunication base stations (BSs), where on-site EC devices with larger resource capacities are deployed, and mobility of vehicles are considered at the same time. Specifically, we propose an Integrated EC framework, which can keep edge resources running across various in-vehicles, RSUs and BSs in a single pool, such that these resources can be holistically monitored from a single control plane (CP). Through the CP, individual in-vehicle, RSU or BS edge resource availability can be obtained, hence applications can be offloaded concerning their resource demands. This approach can avoid execution delays due to resource unavailability or insufficient resource availability at any EC deployment. This research further extends the state-of-the-art by providing intelligent multi-task scheduling, by considering both task dependencies and heterogeneous resource demands at the same time. To achieve this, we propose FedEdge, a variant Bin-Packing optimization approach through Gang-Scheduling of multi-dependent tasks that co-schedules and co-locates multi-task tightly on nodes to fully utilize available resources. Extensive experiments on real-world data trace from the recent Alibaba cluster trace, with information on task dependencies and resource demands, show the effectiveness, faster executions, and resource efficiency of our approach compared to the existing approaches.

The next generation digital subscriber line (DSL) standard G.mgfast introduces far stronger co-channel interference termed as far-end crosstalk (FEXT) than the existing ones. Given perfect transmitter-side channel state information (CSIT), it is well known that the lattice-reduction-aided ${\mathcal{K}}$-best sphere encoder (LR-KBSE) is a near-optimal transmit precoding (TPC) technique compared to the classic (LR-) depth-first sphere encoder (DFSE), albeit having significantly lower complexity than the latter. However, the decision feedback precoding (DFP) structure and the Schnorr-Euchner enumeration procedure, both perceived as state-of-the-art in the literature, are not provably optimal for solving the closest vector problem (CVP) embedded in sphere encoding. As a counterexample, this paper proposes a stochastic sphere encoder (SSE) relying on differential evolution aided random walk over lattices. The parallel processing complexity, memory efficiency and signal to noise ratio (SNR) improvement of the proposed SSE are all shown to be superior to the LR-KBSE for G.mgfast systems.

Data packet routing in aeronauticalad hocnetworks (AANETs) is challenging due to their high-dynamic topology. In this article, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multiobjective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity, as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

This paper investigates the power allocation problem for secure multiple-input single-output transmission with the injection of artificial noise (AN), in the presence of an unknown eavesdropper (Eve). Two power allocation schemes, the optimal adaptive power allocation (OAPA) and suboptimal fixed power allocation (SFPA) schemes, are proposed to enhance the physical layer security of the considered system. Since the noise power at Eve is unknown, both power allocation schemes are designed for the worst-case scenario in which the noise power at Eve is assumed to be zero, aiming to minimize the secrecy outage probability (SOP). To characterize the performance of the proposed power allocation schemes, approximate closed-form expressions for average SOP under a preset noise power level are derived by applying Gauss-Chebyshev quadrature. We also address the worst-case secrecy outage performance for the proposed OAPA and SFPA schemes. Our analytical and numerical results show that, compared with the exhaustive search method that requires Eve’s prior information, the proposed OAPA scheme exhibits comparable secrecy outage performance without Eve’s prior information. Additionally, the SFPA scheme, also without Eve’s prior information, is capable of achieving almost the same worst-case SOP as the OAPA scheme, with a much lower implementation complexity.

Recent research on edge computing (EC) has proposed federated or collaborative learning technique, where machine learning models are shared among participating edge deployments, thereby benefiting from all available datasets without exchanging them. In addition, EC systems are currently exploiting attaching portable edge devices on drones for data processing close to the sources, to achieve high performance, fast response times and real-time insights. Existing researches lack the potential to federate edge resources and manage corresponding service entities running across multiple drones, thus resulting to sub-optimal performance. Therefore, we introduce Aerial Edge, a federated learning-based orchestration framework for a federated aerial EC system. We propose a federated multi-output linear regression model to estimate multi-task resource requirements and execution time, to select the closest drone deployment having congruent resource availability and flight time to execute ready tasks at any given time. For better utilization of resources, we propose a variant bin-packing optimization approach through gang-scheduling of multi-dependent containerized tasks that co-schedules and co-locates tasks tightly on nodes to fully utilize available resources. Extensive experiments on real-world data-trace from Alibaba cluster trace with information on task dependencies show the effectiveness, fast executions, and resource efficiency of our approach.

The flexible deployment without new infrastructure makes unmanned aerial vehicles employing as base stations (UAV-BS) promising for many applications. Since the signals in millimeter-wave frequencies have very small wavelengths, large antenna arrays can be placed in the UAV-BS. Thus, the UAV-BS is capable of providing abundant spatial resources. Spatial modulation (SM) is an effective technology in exploiting additional capacity of the spatial domain by transmitting antenna indices as virtual bits information. However, a limitation of the classical SM is a single transmit antenna activated at each time slot. As a result, the multiplexing gain offered by the multiple transmit antennas has a significant loss. Generalised spatial modulation (GSM) allows several antennas to be activated to overcome the problem of SM. However, GSM has an improvement in throughput, while suffers from the performance loss resulting from the channel correlation, which is generated by multiple active antennas. Thus, the grouping SM (GrSM) is utilized to offer spatial capacity for the UAV-BS in mmWave frequency. Specially, the transmit antennas of the UAV-BS are partitioned into groups based on their channel characteristics. The SM is adopted by each group, and the multiplexing gain is achieved across groups. Moreover, the deployment of the UAV-BS has significant influence on the throughput of the system. In this paper, we formulate a problem to maximize the achievable sum rate of the ground user. The GrSM scheme is utilized to obtain extra throughput in spatial domain. Since the dimension of the UAV position is not very high, a grid based exhaustive search method is adopted to solve the optimization problem. Simulation results demonstrate the proposed solution has an improvement in terms of the sum rate performance.

The secrecy capacity based on the assumption of having continuous distributions for the input signals constitutes one of the fundamental metrics for the existing physical layer security (PHYS) solutions. However, the input signals of real-world communication systems obey discrete distributions. Furthermore, apart from the capacity, another ultimate performance metric of a communication system is its symbol error ratio (SER). In this article, we pursue a radically new approach to PHYS by considering rigorous direct SER optimization exploiting the discrete nature of practical modulated signals. Specifically, we propose a secure precoding technique based on a multi-objective SER criterion, which aims for minimizing the confidential messages' SER at their legitimate user, while maximizing the SER of the confidential messages leaked to the illegitimate user. The key to this challenging multi-objective optimization problem is to introduce a priority factor that controls the priority of directly minimizing the SER of the legitimate user against directly maximizing the SER of the leaked confidential messages. Furthermore, we define a new metric termed as the security-level, which is related to the conditional symbol error probability of the confidential messages leaked to the illegitimate user. Additionally, we also introduce the secure discrete-input continuous-output memoryless channel (DCMC) capacity referred to as secure-DCMC-capacity, which serves as a classical security metric of the confidential messages, given a specific discrete modulation scheme. The impacts of both the channel's Rician factor and the correlation factor of antennas on the security-level and the secure-DCMC-capacity are investigated. Our simulation results demonstrate that the proposed priority-aware secure precoding based on the direct SER metric is capable of securing transmissions, even in the challenging scenario, where the eavesdropper has three receive antennas, while the legitimate user only has a single one.

Massive multiple‐input multiple‐output (MIMO) systems with hundreds of correlated antennas at base station are capable of offering abundant spatial resources. When spatial‐domain modulation is applied to these systems, the spatial modulation (SM) using only one radiofrequency chain benefits practical implementation, but suffers from significantly reduced multiplexing gain offered by the massive number of antennas. By contrast, the generalised SM (GSM) allows to simultaneously activate multiple transmit antennas, which improves spatial multiplexing gain, but degrades the achievable error performance, as its design pays no attention to the antenna correlation. The authors propose an antenna grouped SM (GrSM) scheme, which is capable of circumventing the shortcomings of both the SM and GSM schemes suffered in massive MIMO scenarios. In the proposed GrSM scheme, transmit antennas are partitioned into multiple groups, where the relatively strongly correlated antennas within individual groups are used to implement component SM schemes, while the relatively weakly correlated antennas in different groups are beneficial to obtain multiplexing gain. In order to further improve the spectral efficiency, adaptive modulation is integrated with GrSM to form the adaptive GrSM (AGrSM). The achievable error and spectral efficiency performance of GrSM and AGrSM systems are investigated based on both mathematical analysis and Monte‐Carlo simulations, which are also compared with that of the conventional SM and GSM schemes, when massive MIMO communication scenarios are considered. Their studies and performance results show that GrSM is a promising transmit scheme for massive MIMO, which can outperform both the SM and GSM schemes.

In this paper, we investigate the downlink secure beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semi-definite programming (SDP) relaxation are applied to transform it into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore, a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called S-Procedure and convex approximated techniques, we design an efficient iterative algorithm to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms.

The birth of wireless communication systems nearly a century ago has transformed and redefined the way humans communicate and interact. This transformation has evolved over many years and has brought along not only seamless connectivity for human interactions but also communication between machines and devices. While these communication systems are manmade artifacts, the research community has more recently turned its attention to other communication strategies that have spontaneously evolved in nature.

The engineering vision of relying on the “smart sky” for supporting air traffic and the “internet-above-the-clouds” for in-flight entertainment has become imperative for the future aircraft industry. Aeronautical ad hoc networking (AANET) constitutes a compelling concept for providing broadband communications above clouds by extending the coverage of air-to-ground (A2G) networks to oceanic and remote airspace via autonomous and self-configured wireless networking among commercial passenger airplanes. The AANET concept may be viewed as a new member of the family of mobile ad hoc networks (MANETs) in action above the clouds. However, AANETs have more dynamic topologies, larger and more variable geographical network size, stricter security requirements, and more hostile transmission conditions. These specific characteristics lead to more grave challenges in aircraft mobility modeling, aeronautical channel modeling, and interference mitigation as well as in network scheduling and routing. This paper provides an overview of AANET solutions by characterizing the associated scenarios, requirements, and challenges. Explicitly, the research addressing the key techniques of AANETs, such as their mobility models, network scheduling and routing, security, and interference, is reviewed. Furthermore, we also identify the remaining challenges associated with developing AANETs and present their prospective solutions as well as open issues. The design framework of AANETs and the key technical issues are investigated along with some recent research results. Furthermore, a range of performance metrics optimized in designing AANETs and a number of representative multiobjective optimization algorithms are outlined.

In this treatise, first of all, we conceive a generic multiple-symbol differential sphere detection (MSDSD) solution for both single- and multiple-antenna-based noncoherent schemes in both uncoded and coded scenarios, where the high-mobility aeronautical Ricean fading features are taken into account. The bespoke design is the first MSDSD solution in the open literature that is applicable to the generic differential space-time modulation (DSTM) for transmission over Ricean fading. In the light of this development, the recently developed differential spatial modulation and its diversity counterpart of differential space-time block coding using index shift keying are specifically recommended for aeronautical applications owing to their low-complexity single-RF and finite-cardinality features. Moreover, we further devise a noncoherent decision-feedback differential detection and a channel-state information estimation aided coherent detection, which also take into account the same Ricean features. Finally, the advantages of the proposed techniques in different scenarios lead us to propose for the aeronautical systems to adaptively: 1) switch between coherent and non-coherent schemes; 2) switch between single- and multiple-antenna-based schemes as well as; and 3) switch between high-diversity and high-throughput DSTM schemes.

We propose a regularized zero-forcing transmit precoding (RZF-TPC)-aided and distance-based adaptive coding and modulation (ACM) scheme to support aeronautical communication applications, by exploiting the high spectral efficiency of the large-scale antenna arrays and link adaption. Our RZF-TPC-aided and distance-based ACM scheme switches its mode according to the distance between the communicating aircraft. We derive the closed-form asymptotic signal-to-interference-plus-noise ratio (SINR) expression of the RZF-TPC for the aeronautical channel, which is Rician, relying on a non-centered channel matrix that is dominated by the deterministic line-of-sight component. The effects of both realistic channel estimation errors and of the co-channel interference are considered in the derivation of this approximate closed-form SINR formula. Furthermore, we derive the analytical expression of the optimal regularization parameter that minimizes the mean square detection error. The achievable throughput expression based on our asymptotic approximate SINR formula is then utilized as the design metric for the proposed RZF-TPC-aided and distance-based ACM scheme. Monte-Carlo simulation results are presented for validating our theoretical analysis as well as for investigating the impact of the key system parameters. The simulation results closely match the theoretical results. In the specific example that two communicating aircrafts fly at a typical cruising speed of 920km/h, heading in opposite direction over the distance up to 740km taking a period of about 24 min, the RZF-TPC-aided and distance-based ACM is capable of transmitting a total of 77 GB of data with the aid of 64 transmit antennas and four receive antennas, which is significantly higher than that of our previous eigen-beamforming transmit precoding-aided and distance-based ACM benchmark.

In order to enhance the secrecy performance of the generalized space shift keying (GSSK) visible light communication (VLC) system, in this paper, an optical jamming-aided secrecy enhancement scheme is proposed, in which the source transmitter (S) simultaneously sends both the confidential desired signal and optical jamming signals under the amplitude and power constraints. The optical jamming signals obey the truncated Gaussian distribution for satisfying the constraints. Given the discrete set of channel inputs, the optical jamming-aided GSSK-VLC system's secrecy performance is analyzed. Explicitly, the average mutual information (AMI), the lower bound of AMI and its closed-form approximation as well as the achievable secrecy rate are formulated analytically. Furthermore, the optimal power sharing strategy of the proposed GSSK-VLC systems relying on optical jamming is derived. Closed-form expressions are provided for the optimal power sharing in both the low- and high-SNR regions. Finally, the extensive simulation results are presented to validate our analytical results.

In order to meet the demands of “Internet above the clouds,” we propose a multiple-antenna aided adaptive coding and modulation (ACM) for aeronautical communications. The proposed ACM scheme switches its coding and modulation mode according to the distance between the communicating aircraft, which is readily available with the aid of the airborne radar or the global positioning system. We derive an asymptotic closed-form expression of the signal-to-interference-plus-noise ratio (SINR) as the number of transmitting antennas tends to infinity, in the presence of realistic co-channel interference and channel estimation errors. The achievable transmission rates and the corresponding mode-switching distance-thresholds are readily obtained based on this closed-form SINR formula. Monte-Carlo simulation results are used to validate our theoretical analysis. For the specific example of 32 transmit antennas and four receive antennas communicating at a 5-GHz carrier frequency and using 6-MHz bandwidth, which are reused by multiple other pairs of communicating aircraft, the proposed distance-based ACM is capable of providing as high as 65.928-Mb/s data rate when the communication distance is less than 25 km.

As the ever‐increasing attention to green communication, energy efficiency has become an imperative metric in the emerging massive multiple‐input multiple‐output (MIMO) systems. In order to maximize the energy efficiency, transmit antenna selection has been widely concerned by researchers. In this paper, we investigate the coded cooperation transmission for dynamic transmit antennas aided two‐hop massive MIMO systems. Explicitly, we propose a rate‐less codes aided cooperation scheme for reducing the implementation complexity in the broadcast phase, compared to the fixed‐rate coded cooperation scheme. Furthermore, we develop a partial cooperation scheme in the cooperative phase in order to avoid the low achievable rate caused by the full cooperation, especially when the source‐to‐relay (S‐R) channels are poor. Finally, the number of transmit antennas at the base station (BS) is optimized through theoretical analysis in a metric of maximizing the energy efficiency. Moreover, we also analyze the achievable error performance for different modulations. Our simulation results demonstrate the effectiveness of the proposed scheme.

This paper considers the problem of pilot contamination (PC) in large-scale multi-cell multiple-input multiple-output-aided orthogonal frequency division multiplexing systems. We propose an efficient scheme relying on an optimal pilot design conceived for time-domain channel estimation, which can either completely eliminate PC or significantly reduce it, depending on the channel's coherence time. This is achieved by designing an optimal pilot set allowing us to beneficially group the users in all the cells and to assign a time-shifted pilot transmission to the different groups. Unlike the existing PC elimination schemes, which require an excessively long channel coherence time, our proposed scheme is capable of completely eliminating PC under a much shorter coherence time. Moreover, the existing PC elimination schemes can no longer be used if the channel coherent time is insufficiently large. By contrast, even for extremely short channel coherent time, our scheme can still be implemented to significantly reduce PC. This is particularly beneficial for high velocity scenarios. Our simulation results demonstrate the efficiency of the proposed scheme.

Joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding for space-division multiple-access based orthogonal frequency-division multiplexing communication has to consider both the decision-directed CE optimisation on a continuous search space and the MUD optimisation on a discrete search space, and it iteratively exchanges the estimated channel information and the detected data between the channel estimator and the turbo MUD/decoder to gradually improve the accuracy of both the CE and the MUD. We evaluate the capabilities of a group of evolutionary algorithms (EAs) to achieve optimal or near optimal solutions with affordable complexity in this challenging application. Our study confirms that the EA assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér-Rao lower bound of the optimal channel estimation and the bit error ratio performance of the idealised optimal turbo maximum likelihood (ML) MUD/decoder associated with the perfect channel state information, respectively, despite only imposing a fraction of the complexity of the idealised turbo ML-MUD/decoder.

A Differential Evolution (DE) algorithm assisted Minimum Symbol Error Ratio (MSER) Multi-User Detection (MUD) scheme is proposed for multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing / Space Division Multiple Access (OFDM/SDMA) systems. Quadrature Amplitude Modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. The MSER Cost Function (CF) may be deemed to be the most relevant one for QAM, but finding its minimum is challenging. Hence we propose a sophisticated DE assisted MSER-MUD scheme, which directly minimizes the SER CF of multi-user OFDM/SDMA systems employing QAM. Furthermore, the effects of the DE assisted MSER-MUD's algorithmic parameters, namely those of the population size Ps, of the scaling factor λ and of the crossover probability Cron the number of DE generations required for attaining convergence were investigated in our simulations. This allowed us to directly quantify their complexity. The simulation results also demonstrate that the proposed DE assisted MSER-MUD scheme significantly outperforms the conventional MMSE-MUD in term of the system's overall BER and it is capable of narrowing its BER performance discrepancy with respect to the optimal Maximum Likelihood (ML) MUD to about 4dB, while requiring about 200 times less CF evaluations compared to the optimal ML-MUD scheme.

Stochastic optimization assisted joint Channel Estimation (CE) and Multi-User Detection (MUD) were conceived and compared in the context of multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems. The development of stochastic optimization algorithms, such as Genetic Algorithms (GA), Repeated Weighted Boosting Search (RWBS), Particle Swarm Optimization (PSO) and Differential Evolution (DE) has stimulated wide interests in the signal processing and communication research community. However, the quantitative performance versus complexity comparison of GA, RWBS, PSO and DE techniques applied to joint CE and MUD is a challenging open issue at the time of writing, which has to consider both the continuous-valued CE optimization problem and the discrete-valued MUD optimization problem. In this study we fill this gap in the open literature. Our simulation results demonstrated that stochastic optimization assisted joint CE and MUD is capable of approaching both the Cramer-Rao Lower Bound (CRLB) and the Bit Error Ratio (BER) performance of the optimal ML-MUD, respectively, despite the fact that its computational complexity is only a fraction of the optimal ML complexity.

We considers the beamforming assisted multiple-antenna receiver for space-division multiple-access based multiuser systems that employ high-throughput quadrature amplitude modulation (QAM) signalling. The bit error ratio (BER) expression as the function of the beamformer's weight vector is derived, and the minimum BER (MBER) beamforming receiver is then obtained as the solution of the resulting optimisation problem that minimises the MBER criterion. A simplified conjugate gradient algorithm, which has previously demonstrated its effectiveness in solving the minimum symbol error ratio (MSER) optimisation problem, is employed to solve this MBER optimisation. For high-order QAM, although the bit decision is an inherently more complicated procedure than making a symbol decision, it turns out that the computational complexity of computing the MBER solution is similar to that of computing the MSER solution. As expected, our simulation results show that both the MBER and MSER systems achieve the same BER performance, and they significantly outperform the standard minimum mean squares error based solution.

A differential evolution (DE) algorithm aided iterative channel estimation and turbo multi-user detection (MUD) scheme is proposed for multi-user multi-input multiple-output aided orthogonal frequency-division multiplexing / space-division multiple-access (OFDM/SDMA) systems. The proposed scheme iteratively exchanges the estimated channel information and the detected data between the channel estimator and MUD employing a turbo technique, which gradually improves the accuracy of the channel estimation and the MUD, especially for the first iteration. Quadrature amplitude modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. However, the optimal maximum likelihood (ML)-MUD becomes extremely complex for employment in QAM-aided multi-user systems. Hence, two different DE aided MUD schemes, the DE aided minimum symbol error rate (MSER)-MUD as well as the discrete DE aided ML-MUD, were developed, and their achievable performance versus complexity was characterized. The simulation results demonstrate that the proposed DE aided channel estimator is capable of approaching the Cramer-Rao lower bound with just two or three iterations. The ultimate bit error rate lower-bound of the single-user additive white Gaussian noise scenario has been approached in the range of Eb/ N0≥ 10 dB and Eb/ N0≥ 6 dB for the DE aided MSER-MUD and the discrete DE aided ML-MUD, respectively.

A joint channel estimation and Multi-User Detection (MUD) scheme is proposed for multi-user Multiple-Input Multiple-Output (MIMO) Space Division Multiple Access / Orthogonal Frequency-Division Multiplexing (SDMA/OFDM) systems. We design a Dual Repeated Weighted Boosting Search (DRWBS) scheme for joint channel estimation and MUD, which is capable of providing 'soft' outputs, directly fed to the Forward Error Correction (FEC) decoder. The proposed scheme reduces the complexity of the receiver, since it integrates the channel estimation and MUD into a single module and it forwards the Log-Likelihood Ratios (LLRs) to the channel decoder. It also provides an effective solution to the multi-user MIMO channel estimation and MUD problem in ``rank-deficient'' scenarios, when the number of users is higher than the number of receiver antennas. The simulation results demonstrate that the proposed scheme is capable of attaining a BER performace close to the ideal scenario of the Maximum Likelihood (ML) MUD associated with perfect channel knowledge.

In this paper, a Forward Error Coded (FEC) Decision-Directed (FEC-DD) channel estimation scheme is proposed for Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems which is based on the Expectation-Conditional Maximization Either (ECME) algorithm. The proposed DD technique is combined with the Optimised Hierarchy Reduced Search Algorithm (OHRSA) based Multi-User Detector (MUD) and directly calculates the Maximization-Step (M-Step) by conditionally maximizing the logarithmic likelihood function of the "incomplete" data. We avoid the employment of matrix inversion, since only a single subcarrier's Frequency-Domain CHannel Transfer Function (FD-CHTF) is calculated at a time, since we assume that the other subcarriers' FD-CHTFs are the most recent estimates from the previous iteration of the proposed scheme. Our simulation results have demonstrated that the proposed scheme is capable of reducing the received power requirement by 4dB upon exploiting the error correction capability of a FEC decoder within the ECME loop.

In this paper, a joint channel, carrier-frequency-offset (CFO) and noise-variance estimation scheme is proposed for OFDM systems which is based on Expectation and Maximization (EM) algorithm. The channel parameters are estimated using training sequences incorporated at the beginning of each transmission frame. Based on the assumption that the amplitude and CFO of different paths are independent, the received multipath components may be decomposed into $L$ independent data sets of the $L$ resolvable propagation paths. Hence the associated multi-dimensional minimization problem may be decomposed into separate single-dimensional minimization processes, the maximum likelihood and yet, remains capable of approaching performance at a signifucantly reduced complexity.

A joint channel impulse response (CIR) and noise-variance estimation scheme is proposed for multi-user Multiple-Input-Multiple-Output (MIMO) Orthogonal Frequency-Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems, which is based on the Expectation Maximization (EM) algorithm. Multiple users communicating over time-invariant and/or time-variant channels are considered in this paper. Channel estimation becomes quite challenging in this scenario, since an increased number of independent transmitter-receiver links having different statistical characteristics have to be simultaneously estimated for each subcarrier. The proposed EM-based joint CIR and noise-variance estimator designed for multi-user MIMO OFDM/SDMA system is shown to simultaneously estimate the time-invariant and time-variant CIRs as well as noise-variance.

A decision-directed (DD) channel estimation based on iterative linear minimum mean square error (LMMSE) is proposed for orthogonal frequency division multiplexing systems. Existing DD channel estimation is well known to have the problem of error propagation because of symbol-by-symbol detection. The proposed algorithm can estimate the correction term of current channel state information (CSI) according to the error vector of previous CSI by applying the orthogonality principle, and corrects the current CSI with this correction term. Analysis and simulation results have shown that this method has no error propagation problem. The performance of the proposed algorithm is much better than the conventional DD channel estimation, and close to the optimal LMMSE estimator, but with much less computational complexity compared with the optimal LMMSE estimator.