Non-orthogonal multiple access (NOMA)-enabled incremental relaying can significantly enhance spectrum efficiency and boost the performance of cognitive radio (CR) networks. However, imperfect channel state information (CSI) due to estimation errors, and hardware impairments (HIs) caused by phase noise, quantization errors, and nonlinearities, introduce new complexities in CR-NOMA networks. In this work, we investigate the transmission strategy in HIs and imperfect CSI built-in cooperative NOMA networks. First, a system model with HIs and imperfect CSI is designed, in which multiple CR relays coexist to achieve the incremental relaying. Second, a multi-relay incremental relaying strategy is proposed, where all relays (successfully decoding the information to the destination) can cooperate to forward the information, thereby improving the network performance. More especially, the exact and asymptotic expressions of outage probability as well as the throughput are explicitly derived. Third, to get a comparable strategy, a best-relay incremental relaying strategy is further constructed, selecting the one with the highest signal-to-noise ratio (SNR) among all successful relays to assist the destination. Finally, the correctness of theoretical analyses for both strategies are verified by simulations, and the impact of different network parameters on performance are validated through simulations.

In recent years, orthogonal time frequency space (OTFS) technique has garnered substantial academic attention as a promising solution for ensuring robust and reliable communication in high-mobility wireless communication environments. In this paper, we present a complex-valued graph neural network (CV-GNN) aided signal detection scheme for OTFS modulation, which can mitigate the channel spreading caused by fractional Doppler shifts. To mitigate inter-carrier interference (ICI) and inter-symbol interference (ISI) induced by fractional Doppler shifts and imperfect channel state information, the proposed detector is able to process the received OTFS signal in the complex plane to acquire the complete phase information of effective channel. Simulation results demonstrate that the proposed method can outperform other state-of-the-art schemes by 1∼4 dB in terms of reliability performance.

Integrated air-ground communication (IAGC) has emerged as a promising solution to deliver seamless wireless coverage and high-data-rate services. However, potential malicious eavesdroppers pose a serious threat to the confidential transmission in IAGC due to their non-cooperative behaviors and the inherent openness of communication channels. To tackle this problem, a dynamic spectrum control (DSC)-based transmission scheme is proposed to enhance covert performance and communication reliability in IAGC. With the proposed scheme, we apply the principles of block cryptography, perform adaptive iterative and orthogonal transformations to generate sequence sets that drive transmission decisions. Guided by these sequences, multiple legitimate users can dynamically occupy different frequency slots and transmit data simultaneously. In addition, we analyze the probability of frequency slot multiplexing when several data groups occupy the same frequency slot in a time slot, resulting in the closed-form expression for the detection error probability. We then derive the maximum reliable transmission probability and ergodic rate subject to the covert communication constraints. Simulation results demonstrate that the proposed scheme can achieve superior covert performance compared with benchmark schemes. Furthermore, we evaluate and discuss the effects of key parameters in the proposed DSC-based transmission scheme on communication security and reliability.

The open and vulnerable nature of wireless channels exacerbates security risks in integrated sensing and communication (ISAC) systems, especially when the sensing targets act as potential eavesdroppers (Eves), and these risks intensify with collusion among Eves. To address this challenge, this paper investigates a novel strategy for a robust reconfigurable intelligent surfaces (RIS)-assisted secure ISAC system, where an ISAC base station facilitates simultaneous secure communication with legitimate users and sensing of multiple targets that may serve as Eves. We examine two different interaction mechanisms among Eves, namely, non-colluding Eves (NCE) and colluding Eves (CE), under both perfect and imperfect channel state information (CSI) assumptions. For both mechanisms, we formulate the optimization problem of maximizing users’ sum secrecy rate by jointly designing the transmit beamforming and RIS phase-shifts. This optimization is subject to constraints on transmit power, sensing requirements, and unit-modulus RIS phase shifts. The resulting non-convex problems are solved via alternating optimization (AO) algorithms. Specifically, in order to handle the severely non-convex and coupled objective function and multi-link accumulated channel error constraints caused by CE as well as imperfect CSI, we employ the majorizationminimization algorithm and the S-procedure to convert these problems into tractable forms. Simulation results validate the effectiveness of our proposed algorithms. We highlight that, at the expense of a 15% reduction in the users’ sum rate, our proposed algorithm achieves up to a 185% increase in the sum secrecy rate. Furthermore, we quantify the sensing-security trade-off by analyzing the reduction of the sum secrecy rate induced by sensing requirements, and we reveal the impacts of various factors on the sum secrecy rate, such as RIS element number, channel estimation errors, and sensing thresholds.

Affine frequency division multiplexing (AFDM) is a promising chirp-assisted multicarrier waveform for future high-mobility communications. This paper is devoted to enhanced receiver design for multiple-input–multiple-output AFDM (MIMO-AFDM) systems. Firstly, we introduce a unified variational inference (VI) approach to approximate the target posterior distribution, under which the belief propagation (BP) and expectation propagation (EP)-based algorithms are derived. As both VI-based detection and low-density parity-check (LDPC) decoding can be expressed by bipartite graphs in MIMO-AFDM systems, we construct a joint sparse graph (JSG) by merging the graphs of these two for low-complexity receiver design. Then, based on this graph model, we present the detailed message propagation of the proposed JSG. Additionally, we propose an enhanced JSG (E-JSG) receiver based on the linear constellation encoding model. The proposed E-JSG eliminates the need for interleavers, de-interleavers, and log-likelihood ratio transformations, thus leading to concurrent detection and decoding over the integrated sparse graph. To further reduce detection complexity, we introduce a sparse channel method by approaximating multiple graph edges with insignificant channel coefficients into a single edge on the VI graph. Simulation results show the superiority of the proposed receivers in terms of computational complexity, detection and decoding latency, and error rate performance compared to the conventional ones.

The rise of heterogeneous aerial and space platforms within Space-Air-Ground Integrated Networks (SAGINs) introduces significant challenges, as the limited spectrum resources force these platforms to operate within shared frequency bands, resulting in co-existing systems. Effective interference management in such networks requires both the design of communication channels and the dynamic mitigation of interference between them. Prior research has largely focused on interference mitigation with fixed communication links, often overlooking adaptive channel selection, which can result in performance degradation. In this study, we address this limitation by introducing MetaRS, an innovative, self-intelligent rate-splitting solution designed for more flexible interference management in co-existing SAGINs. MetaRS enables adaptive channel and communication scheme selection, by leveraging a Fully-Distributed Rate-Splitting Multiple Access (FD-RSMA)-based framework enhanced with a one-pass diffusion model. Specifically, the FD-RSMA-based framework allows MetaRS to dynamically shift its interference management strategy according to the current network status. The integration of the diffusion model further enhances MetaRS by allowing it to recognize and adapt to real-time channel conditions and user deployment, thereby enabling self-intelligent interference mitigation. Simulation results demonstrate that MetaRS significantly outperforms conventional SDMA, RSMA, and FD-RSMA approaches. This improvement stems from MetaRS’s joint optimization of channel selection and its adaptive, intelligent interference management capabilities, which effectively balance channel utilization and mitigate interference in complex, multi-platform environments.

Unmanned aerial vehicle (UAV) data collection problems in wireless sensor networks (WSNs) under random and uncertain environments are critical challenging, due to massive burden of real-time communication among UAVs for aligning with observation and state information, ect. For this sake, this work investigates the UAV cooperation problem of WSN data collection by jointly maximizing collected data amount while minimizing cooperation cost. We formulate the problem as a constrained partially observable Markov decision process (CPO-MDP), which stimulates the design of a novel safe reinforcement learning aided lightweight cooperation (SRL-LC) framework for multi-UAV data collection. Speficially, the self-conscious cooperative communication scheme is developed to assist the optimization of the UAV trajectory decision making. Additionally, a safety module embedded in the decision network integrates a relaxed artificial potential field (APF) algorithm, enabling UAVs to maintain safety distance constraints during training. Simulation results demonstrate that the proposed SRL-LC framework achieves data collection performance comparable to the full-cooperation scheme for various settings of prior information, while reducing communication cost by approximately 85%. Moreover, the SRL-LC framework ensures zero violations of safety constraints throughout the training process.

This paper investigates a multi-user uplink mobile edge computing (MEC) network, where the users offload partial tasks securely to an access point under the non-orthogonal multiple access policy with the aid of a reconfigurable intelligent surface (RIS) against a multi-antenna eavesdropper. We formulate a non-convex optimization problem of minimizing the total energy consumption subject to secure offloading requirement, and we build an efficient block coordinate descent framework to iteratively optimize the number of local computation bits and transmit power at the users, the RIS phase shifts, and the multi-user detection matrix at the access point. Specifically, we successively adopt successive convex approximation, semi-definite programming, and semidefinite relaxation to solve the problem with perfect eavesdropper’s channel state information (CSI), and we then employ S-procedure and penalty convex-concave to achieve robust design for the imperfect CSI case. We provide extensive numerical results to validate the convergence and effectiveness of the proposed algorithms. We demonstrate that RIS plays a significant role in realizing a secure and energy-efficient MEC network, and deploying a well-designed RIS can save energy consumption by up to 60% compared to that without RIS. We further reveal impacts of various key factors on the secrecy energy efficiency, including RIS element number and deployment position, user number, task scale and duration, and CSI imperfection.

In this paper, we analyse the reliability performance for the orthogonal time frequency space (OTFS) based low earth orbit (LEO)-satellite-air-terrestrial (LSAT) communication system. To facilitate the downlink transmission from the LEO satellite to the terrestrial node, a group of randomly distributed mobile unmanned aerial vehicles (UAVs) are employed to serve as the relays with decode-and-forward (DF) scheme. With the aid of stochastic geometry approach, the distribution of mobile UAVs is modeled by Poisson point processes (PPP) process with two motion modes: user dependent model (UDM) and user independent model (UIM). We derive the approximate closed-form expressions for the outage probabilities of the LSAT system under two UAV motion modes. Finally, the simulation results demonstrate that the reliability of the LSAT system can be significantly enhanced by using OTFS scheme, and by properly adjusting the UAV deployment parameters, corroborating the theoretical derivation.

Integrating non-terrestrial networks (NTN) with terrestrial networks (TN) is vital to support scalable multicast/broadcast services (MBS) in 6G, particularly for low-altitude UAV swarms requiring seamless and reliable coverage. Low Earth orbit (LEO) constellation in integrated TN-NTN can effectively take over multicast to UAVs when flying over TN underserved regions. However, distinct differences in signal variation and mobility between TN and NTN make it difficult to optimally exploit MBS cooperation and maintain superior delivery. To address these challenges, this paper proposes a mobility-aware TN-NTN MBS orchestration framework for low-altitude UAVs. We fist cognize signal variations of TN and NTN in low-altitude layer with UAV mobility characteristics from cell center to edge, and use an Adaboost-based machine learning classifier to dynamically group UAVs into two segments for optimal system multicast delivery. A joint file multicast scheduling strategy is also proposed to align with UAV and NTN mobility-driven grouping dynamics to globally enhance multicast time efficiency. System-level case studies with a practical LEO constellation confirm our approach significantly outperforms existing methods, especially when more UAVs near cell edges. Our method also demonstrates strong adaptability to network dynamics and superior time efficiency, enabling robust and efficient MBS delivery in integrated 6G TN-NTN systems.

Millimeter wave (mmW) is a promising technology for the next generation of mobile communications. However, the transmission efficiency and communication reliability of heterogeneous mobility user networks are limited by the frequent mmW beam alignment and the severe Doppler shift in the time-varying channel, respectively. To address this challenge, a joint resource allocation in frequency domain, time domain and power domain is investigated for the mmW communication network based on non-orthogonal multiple access (NOMA) and orthogonal time-frequency space (OTFS) techniques. The average spectral efficiency of high-mobility user equipment (H-UE) is maximized by the joint optimization of UE scheduling, beamwidth and transmit power. In order to solve the non-convex mixed integer problem of rate maximization, we propose the multi-dimensional resource allocation scheme based on alternate optimization method (AO-MRA), which decouples the initial intractable problem into two solvable sub-problems. In particular, based on the majorization-minimization approach, the scheduling algorithm is proposed to find the best UE scheduling for the NOMA groupings of heterogeneous mobility UEs, and the joint beamwidth and transmit power (JBP) algorithm is further designed for the optimal transmission time and power of base station in the mmW communication network. The proposed AO-MRA scheme can obtain effective suboptimal solutions of the initial problem. Simulation results demonstrate that the AO-MRA scheme is superior to other benchmark schemes in maximizing transmission efficiency. Moreover, the AO-MRA scheme is more suitable for low-power and high-bandwidth situations, and has superior spectral efficiency in terms of delay and Doppler high-resolution.

This paper investigates the amalgamation of affine frequency division multiplexing (AFDM) with sparse code multiple access (SCMA), termed as AFDM-SCMA, to facilitate massive connectivity in high-mobility scenarios. We start by introducing the basic principles of SCMA and AFDM systems and then present the proposed AFDM-SCMA system with multiple input and multiple output (MIMO) for both downlink and uplink channels. A two stage detector is proposed for the multi-user detection of the downlink channels. Additionally, to reduce the detection complexity and exploit the channel sparsity, we propose an expectation propagation algorithm (EPA)-aided low complexity receiver for uplink channels. Through numerical simulations, we validate the enhanced performance of the proposed AFDM-SCMA systems compared to conventional orthogonal frequency division multiplexing-empowered SCMA (OFDM-SCMA) systems in terms of error rate performance.

In order to overcome the vicious competition between different high-volume services, we study the wireless resource sharing problem in the transmission process of the MEC-caching-coexist (MCCe) system with the capability of mmWave communications. The multiobjective Markov decision process (MOMDP) is introduced to model the task scheduling and resource allocation problem for the mmWave links, which aims to minimize the transmission delay and energy consumption simultaneously. Note that, for practical consideration, the exact channel information of all links are not known. We propose a novel multiobjective deep reinforcement learning with discrete-continuous hybrid action space (MODRL/HA) algorithm. In particular, the envelope updated design (EUD) is designed to realize the multiobjective optimization from the perspective of the Bellman operator. On the other hand, the parameterized network design (PND) is developed to deal with the hybrid action space of discrete task scheduling and continuous beamwidth and power variables. Our simulations show that, the MODRL/HA algorithm can improve 22% performance in terms of the tradeoff between delay and energy consumption compared with the benchmark schemes, which are original deep deterministic policy gradient (DDPG) and multiobjective DDPG (MODDPG) algorithms.

This paper investigates the data collection problem for multi-functional unmanned aerial vehicle (UAV) swarm in a dynamic wireless sensor network (WSN), where sensors have different mobility profiles. For a practical consideration, the observation information of the UAVs is limited, and has the risk of obsolescence, under the limited battery life. The considered optimization problem is formulated as a partially observable Markov decision process (POMDP), which includes the discrete on-off variables of collection, radar, communication and movement, and the continuous variables of the transmit power, UAV flying direction and velocity. For solving the problem, we propose a multi-agent hybrid proximal policy with reward shaping and pre-training optimization algorithm (MAHPPO-RSP). In particular, the proposed algorithm is performed through a two-step training way of supervised learning and reinforcement learning, upon introducing both human experience and autonomous learning. The provided results show that the proposed MAHPPO-RSP algorithm exhibits a stable convergence manner. Furthermore, it obtains a promising trade-off between data collection and energy consumption, outperforming two baseline schemes.

Non-terrestrial networks (NTN) provide a revolutionary solution to bridge the digital divide in areas underserved by terrestrial network (TN). Particularly, low Earth orbit (LEO) constellations can substitute for offering data services to mobile devices like UAVs when flying into TN service-deficient areas. In this paper, viewing TN and NTN as both competitors and collaborators, we present a novel approach to optimize UAV mobility management in integrated TN and NTN, thereby improving network service continuity. Specifically, we enable UAVs to opportunistically handover (HO) between TN and NTN during flight to maintain reliable data reception while minimizing HO overhead. The decision to switch from TN to NTN involves comparative assessments of service capabilities and HO rates between two segments over time, considering their link quality variations during UAV flight, TN coverage distributions, and orbital dynamics of LEO satellites. Our system-level case studies, based on a practical LEO constellation, demonstrate the significant advantages of UAV HO planning in integrated TN and NTN over standalone TN or NTN for HO numbers and service rates. We also demonstrate that in various scenarios, our UAV mobility management solution consistently outperforms existing heterogeneous HO methods that underrate the dynamic differences in service capabilities between TN and NTN.

As for the time-division communications system, the pilot spoofing attack (PSA) technique is maliciously utilized by active eavesdroppers during the uplink training phase, for contaminating the legitimate channel estimation and thus altering the beamforming design towards the eavesdroppers. Nonorthogonal multiple access (NOMA) has been recognized as the key technology for the envisioned Internet of Things (IoT) networks. In order to prevent the aforementioned information leakage in NOMA-IoT systems, we develop a novel two-way training scheme to detect PSA and a robust secure beamforming design for providing secure transmission, by utilizing the emerging technique of reconfigurable intelligent surface (RIS), which is turned off during the uplink training phase and turned on during the downlink training phase, respectively. Considering that the perfect channel state information related to the eavesdropping channel is typically difficult to obtain, a secrecy outage probability-constrained robust secure beamforming design is proposed to maximize the achievable sum secrecy rate of the legitimate users, by alternatively optimizing the active beamforming and RIS passive beamforming, while satisfying the requirements of the NOMA transmission. Elaborate simulation results reveal that the proposed detection method attains a super PSA detection performance and the proposed robust secure beamforming design is capable of efficiently enhancing the achievable sum secrecy rate, compared with various benchmark schemes.

This article investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide Internet of Things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase-shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA)-based algorithm is used for the RIS phase-shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.

How to achieve reliable communication and safety monitoring is very important in coal mines. However, most of the existing transmission strategies and sensing-based monitoring approaches assume a single objective and neglect non-line-of-sight (NLOS) problems brought by winding tunnels or mine collapses. To this end, we first propose a multihop reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) approach to maximize the energy efficiency of the JCAS access point and the sum sensing rates in order to improve the sensing accuracy. Specifically, we formulate an energy-efficient optimization problem by jointly designing both the phase-shift matrix and the switches status of the RISs as well as the transmit power of the access point. The problem is solved by adopting the successive convex approximation-based alternating optimization algorithm, the second-order optimization method, the Lambert-$w$function, and Newton’s method. Moreover, a sensing-based rate optimization problem is also solved via the Lagrange relaxation method and the Gradient descent method. Simulation results demonstrate that the proposed algorithm has better robustness and higher energy efficiency.

Upon exploiting massive spectrum resources, millimeter-wave (mmW) communication can significantly improve the transmission rate of mobile-edge computing (MEC) offloading, whereas the directional mmW links are constrained by shrunk beam coverage and demand extra phase for beam alignment. To enhance the accessing efficiency, we develop the nonorthogonal multiple access (NOMA) scheme-based mmW MEC mechanism, namely, NOMA-mmW MEC, therefore motivating to minimize the average delay of the MEC offloading, by jointly optimizing the beamwidth, user equipment (UE) scheduling, and transmit power. To tackle the mixed-integer nonlinear programming (MINLP) problem of delay minimization, we develop the alternative optimization (AO) approach-based RA scheme, namely, AO-RA, to obtain the close-optimum solutions. In the AO-RA scheme, we propose the matrix control many-to-one with externality (MC-M2OE) algorithm, to find the best UE scheduling for the NOMA groupings of different types of UEs. Upon the above, we further design the joint beamwidth and transmit power (JBTP) algorithm, which determines the optimal beamwidth and transmit power for the MEC offloading transmissions. Our simulation results show the effectiveness of the proposed AO-RA scheme in minimizing the offloading delay, where our MC-M2OE and JBTP algorithms can significantly outperform the existing approaches. From the simulation results, we may conclude that it needs to carefully address the tradeoff between beam alignment overhead and transmission gain while properly balancing the loading among different NOMA groups, for the practical consideration of NOMA-mmW MEC technology.

This paper investigates computing task offloading from an Internet-of-Thing (IoT) device with limited transmit power to a mobile-edge computing (MEC) server located beyond the communication range of the IoT device. We propose an opportunistic cooperative offloading (OCO) strategy that recruits the IoT device’s nearby spatially random idle-state users as helpers and opportunistically schedules one of them to partially execute the latency-critical task, and forwards the rest portion of the task to the MEC server. For the OCO strategy, we investigate the helper scheduling under three cases of system information availability, i.e., the global, partial, and distance information cases, and develop an offloading-outage optimal scheduling scheme for each case. For each scheduling scheme, an approximate expression is derived for the offloading-outage probability, with which the achieved diversity order is also theoretically evaluated. Simulation results verify our performance analysis for the OCO strategy using helper scheduling and show its achieved offloading-outage/energy consumption reduction over multi-helper cooperative offloading that fully uses all helpers for cooperation. In addition, the advantages of the proposed helper scheduling schemes over existing scheduling schemes are also demonstrated by simulations.

This paper exploits an intelligent reflecting surface (IRS) assisted wireless powered mobile edge computing and caching (WP-MECC) network. In particular, an IRS is utilized to reflect energy signals from a power station (PS) to various IoT devices for energy harvesting during uplink wireless energy transfer (WET). These devices collect energy to support their own partially local computing for computational tasks and their offloading capabilities to an access point (AP), with the help of IRS via time or frequency division multiple access (TDMA or FDMA). The AP is equipped with a local cache connected with a MEC server via a backhaul link, which prefetches the data to facilitate edge computing capabilities. The maximization of a utility function is formulated to evaluate the overall network performance, which is defined as the difference between the sum of computational bits (offloading bits and local computing bits) and total backhaul cost. Due to multiple coupled variables, we first design the optimal caching strategy. Then, an auxiliary vector is introduced to coordinate the energy consumption of local computing and offloading, where its optimal solution can be achieved by an exhaustive search. Moreover, we utilize the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling for the TDMA scheme or the optimal bandwidth allocation for the FDMA counterpart in closed form. The IRS phase shifts are iteratively designed by employing the quadratic transformation (QT) and the Riemannian Manifold Optimization (RMO). Finally, simulation results are demonstrated to validate the network utility performance and confirm the advantage of the employment of IRS, the optimal IRS phase shift design and caching strategy, in comparison to the benchmark schemes.

This paper investigates the secure hybrid beamforming (HB) design in an intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) system, where an IRS is deployed to help the legitimate transmission from Alice to Bob under the eavesdropping of Eve. To protect the legitimate transmission, Alice employs HB to send both the information signal and the artificial noise, while the IRS employs passive beamforming (PB) to reconstruct the wireless environment. Aiming at the secrecy capacity (SC) maximization, the joint optimization of HB and PB is formulated as a non-convex problem with constant-modulus constraints. To efficiently solve such a challenging problem, the original problem is decomposed into a PB subproblem and an HB subproblem, then these subproblems are sequentially solved by the proposed algorithms. Particularly, for the PB subproblem, we propose a channel information aided PB algorithm, which is proved to converge at a stationary point. With the solution of PB subproblem, two algorithms are proposed for the HB subproblem: 1) near-optimal SC approaching HB algorithm that achieves a near-optimal solution; 2) low-complexity HB algorithm that achieves a slight lower SC with less computational complexity. Simulation results demonstrate the superior performance of proposed algorithms in comparison with the state-of-the-art works.

With exploiting massive spectrum resources, millimeter wave (mmWave) communications significantly improve the offloading capability for future mobile edge computing (MEC) techniques, which however is constrained by blockage problem in dynamic environments. In this paper, we study the resource allocation problem for the conceived mmWave MEC system with dynamic offloading process, in which the UEs are characterized by being mobile and having the imperfect knowledge of the offloading tasks coming. By introducing the multi-objective Markov decision process (MOMDP), the resource allocation problem is modeled by simultaneously minimizing the delay and energy consumption, where jointly considering the multi-beam assignment (mBA) and beamwidth and power optimization (BPO). To tackle this problem, we innovatively propose a matching-aided-learning (MaL) resource allocation scheme, with the aid of a learnable weight based attention mechanism (LW-AM) for adapting the dynamic offloading process. In particular, our MaL scheme includes many-to-one matching (M2O-M) based mBA algorithm and deep deterministic policy gradient (DDPG) based BPO algorithm, which are executed iteratively and converge with relatively low number of iterations. The simulation results show the practical value of the proposed MaL, which can approach the performance of benchmark scheme with perfect knowledge of offloading tasks.

Covert communication is able to provide high-level security by protecting communication behavior. In this paper, we develop a covert cooperative cognitive radio (CCCR) network, where primary transmitter (PT) transmits information with the aid of multiple secondary transmitters (STs). In return, STs are able to transmit private information by exploiting PT's spectrum in presence of a powerful eavesdropper (Eve). Meanwhile, we propose a cognitive user scheduling scheme based on link information and maximum-minimum principle. Moreover, we derive Eve's expected detection error probability and evaluate the covert performance of the novel scheme. Numerical results show that joint impact of self-interference and jamming power of Eve can enable STs to achieve covert transmission. Furthermore, it can be found that the influence of the interference power on Eve's detection error probability and covert performance is significant when the self-interference cancellation coefficient is sufficient large.

In this article, we propose an intelligent reflecting surface (IRS)-enabled wireless-powered caching system. In the proposed IRS model, a power station (PS) provides wireless energy to multiple Internet of Things (IoT) devices, delivering their information to an access point (AP) by utilizing the harvested power. The AP, equipped with a local cache, stores the IoT data to avoid waking up the IoT devices frequently. Meanwhile, we deploy the IRS involving in the wireless energy and information transfer process for performance enhancements. In this practical system, the PS and AP could belong to different service providers. Also, the AP requires to incentivize the PS to offer a provisional energy service. We model the interaction between the PS and AP as a Stackelberg game that jointly optimizes the transmit power of the PS, the energy price, the phase shifts of the wireless energy transfer (WET) and wireless information transfer (WIT) phases, as well as wireless caching strategies of the AP. In this way, we first derive the optimal solutions of the phase shifts and the transmit power of the PS in a closed form. We propose an alternating optimization (AO) algorithm to optimize the wireless caching strategies and the energy price iteratively. Finally, we present various numerical evaluations to validate the beneficial role of the IRS and the wireless caching strategies and the performance of the proposed scheme compared with the existing benchmark schemes.

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

In this article, we consider covert beamforming design for intelligent reflecting surface (IRS)-assisted Internet-of-Things (IoT) networks, where Alice utilizes IRS to covertly transmit a message to Bob without being recognized by Willie. We investigate the joint beamformer design of Alice and IRS to maximize the covert rate of Bob when the knowledge about Willie’s channel state information (WCSI) is perfect and imperfect at Alice, respectively. For the former case, we develop a covert beamformer under the perfect covert constraint by applying semidefinite relaxation. For the latter case, the optimal decision threshold of Willie is derived, and we analyze the false alarm and the missed detection probabilities. Furthermore, we utilize the property of the Kullback–Leibler divergence to develop the robust beamformer based on a relaxation,$S$-Lemma, and alternate iteration approach. Finally, the numerical experiments evaluate the performance of the proposed covert beamformer design and robust beamformer design.

Toward 6G networks, such as virtual reality (VR) applications, Industry 4.0, and automated driving, demand mobile-edge computing (MEC) techniques to offload computing tasks to nearby servers, which, however, causes fierce competition with traditional communication services. On the other hand, by introducing millimeter wave (mmWave) communication, it can significantly improve the offloading capability of MEC, enabling low latency and high throughput. For this sake, this article investigates the resource management for the offload transmission of the mmWave MEC system, when considering the data transmission demands from both communication-oriented users (CM-UEs) and computing-oriented users (CP-UEs). In particular, the joint consideration of user pairing, beamwidth allocation, and power allocation is formulated as a multiobjective problem (MOP), which includes minimizing the offloading delay of CP-UEs and maximizing the transmission rate of CM-UEs. By using the$\epsilon $-constraint approach, the MOP is converted into a single-objective optimization problem (SOP) without losing Pareto optimality, and then the three-stage iterative resource allocation algorithm is proposed. Our simulation results show that the gap between Pareto front generated by the three-stage iterative resource allocation algorithm and the real Pareto front is less than 0.16%. Furthermore, the proposed algorithm with much lower complexity can achieve the performance similar to the benchmark scheme of NSGA-II, while significantly outperforms the other traditional schemes.

This paper investigates the security performance of a joint unicast-multicast streaming system, where different users present heterogeneous mobilities. The orthogonal time frequency space (OTFS) scheme is employed to overcome severe Doppler effect caused by high mobility. The closed-form expression is derived for the maximum secrecy rate of unicast transmission with high privacy. Furthermore, the positive secure capacity probability (PSCP) of unicast transmission is also obtained and analyzed. Our analytical results show that compared with high-mobility eavesdroppers, low-mobility eavesdroppers pose a greater threat to unicast secrecy. Moreover, when the outage probability of unicast is greater than 1/2, more time frequency (TF) resources should be allocated to unicast, in order to guarantee the security performance of unicast.

In this paper, we investigate the optimal probabilistic constellation shaping design for covert communication systems from a practical view. Different from conventional covert communications with equiprobable constellations modulation, we propose non-equiprobable constellations modulation schemes to further enhance the covert rate. Specifically, we derive covert rate expressions for practical discrete constellation inputs for the first time. Then, we study the covert rate maximization problem by jointly optimizing the constellation distribution and power allocation. In particular, an approximate gradient descent method is proposed for obtaining the optimal probabilistic constellation shaping. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes a lower bound on the achievable rate where the optimal probabilistic constellation shaping problem can be solved efficiently using the Frank-Wolfe method. Extensive numerical results show that the optimized probabilistic constellation shaping strategies provide significant gains in the achievable covert rate over the state-of-the-art schemes.

Covert transmission is studied for an intelligent reflecting surface (IRS) aided communication system, where Alice aims to transmit messages to Bob without being detected by the warden Willie. Specifically, an IRS is used to increase the data rate at Bob under a covert constraint. For the considered model, when Alice is equipped with a single antenna, the transmission power at Alice and phase shift at the IRS are jointly optimized to maximize the covert transmission rate with either instantaneous or partial channel state information (CSI) of Willie's link. In addition, when multiple antennas are deployed at Alice, we formulate a joint transmit beamforming and IRS phase shift optimization problem to maximize the covert transmission rate. One local optimal algorithm and two low-complexity suboptimal algorithms are proposed to solve the problem. Furthermore, for the case of imperfect CSI of Willie's link, the optimization problem is reformulated by using the triangle and Cauchy-Schwarz inequalities. The reformulated optimization problems are solved using an alternative algorithm, semidefinite relaxation (SDR) and Gaussian randomization techniques. Finally, simulations are performed to verify our analysis. The numerical results show that an IRS can degrade the covert transmission rate when Willie is closer to the IRS than Bob.

This article investigates the intelligent reflecting surface (IRS)-aided multi-antenna covert communications. In particular, with the help of an IRS, a favorable communication environment can be established via controllable intelligent signal reflection, which facilitates the covert communication between a multi-antenna transmitter (Alice) and a legitimate full-duplex receiver (Bob) in the existence of a watchful warden (Willie). In order to shelter the desired communication, Bob generates jamming signals with a varying power to confuse Willie. The beamforming vector employed by Alice and the passive phase shifts of the IRS are optimized jointly to maximize the covert rate under the constraints of the successful detection probability at Willie and the communication outage experienced by Bob. We focus on the worst case by characterizing the minimum successful detection probability at Willie. The formulated problem is non-convex, due to the coupling between the beamforming vector of Alice and the phase shifts of the IRS, and the unit modulus constraint on the phase shifts of the IRS. To tackle the above issues, we first employ the penalty dual decomposition (PDD) method to handle the coupling effect. After that, we apply the successive convex approximation (SCA) method to develop an iterative algorithm for locating a Karush-Kuhn-Tucker (KKT) solution of the joint design problem. Moreover, we show that our proposed iterative algorithm can be adapted to handle the multi-antenna Willie case. Simulation results validate the effectiveness of the proposed iterative algorithm and show the great potential brought by the IRS for covert communications.

Covert communication provides high-level security for protecting users' privacy information. In this paper, we analyze the joint impact of an external jammer and channel uncertainty on covert communication in multi-user cognitive radio networks. Meanwhile, to fairly schedule the covert communication over multi-user cognitive radio networks, we propose a fairness secondary user (SU) scheduling scheme, which enables each SU to have the same probability for sending information covertly with the aid of an external jammer. Then, the closed-form expression for the covert rate of the scheduled SU can be obtained. Our results show that the minimal detection error probability and covert rate of the scheduled SU can be significantly improved by exploiting the channel uncertainty and random variation of interference power. Moreover, the impact of interference power on the probability of detection error and the covert rate is noticeable when channel uncertainty is large.

To explore the potential of machine learning‐based cooperative spectrum sensing (CSS) in training time, classification speed and classification performance, this study mainly focuses on studying the problem of the feature vectors selecting for machine learning‐based CSS. First, a new machine learning‐based CSS framework is presented, in which, energy vector forming module, feature vector conversion module, training module, classification module and training sample database are included. Second, a new two‐dimensional distance vector is developed, and it is converted by an m ‐dimensional energy vector according to the distance measurement between vectors. Furthermore, six combination modes are obtained by combining three feature vectors (energy, probability and distance vectors) with two supervised machine learning methods, which are support vector machine (SVM) and weighted K‐nearest‐neighbour, respectively. From the proposed experimental simulations, the authors can find that the distance vector is obviously superior to the probability vector in computation time. Moreover, the probability vector and distance vector are superior to the energy vector in training time except for the case of poor signal and fewer users, and obviously superior to the energy vector in classification speed. At last, the probability vector and distance vector with SVM classifier show the best classification performance in six combination modes.

This paper investigates the hybrid precoding design in millimeter-wave (mmWave) systems with a fully-adaptive-connected precoding structure, where a switch-controlled connection is deployed between every antenna and every radio frequency (RF) chain. To maximally enhance the energy efficiency (EE) of hybrid precoding under this structure, the joint optimization of switch-controlled connections and the hybrid precoders is formulated as a large-scale mixed-integer non-convex problem with high-dimensional power constraints. To efficiently solve such a challenging problem, we first decouple it into a continuous hybrid precoding (CHP) subproblem. Then, with the hybrid precoders obtained from the CHP subproblem, the original problem can be equivalently reformulated as a discrete connection-state (DCS) problem with only 0-1 integer variables. For the CHP subproblem, we propose an alternating hybrid precoding (AHP) algorithm. Then, with the hybrid precoders provided by the AHP algorithm, we develop a matching assisted fully-adaptive hybrid precoding (MA-FAHP) algorithm to solve the DCS problem. It is theoretically shown that the proposed MA-FAHP algorithm always converges to a stable solution with the polynomial complexity. Finally, simulation results demonstrate the superior performance of the proposed MA-FAHP algorithm in terms of EE and beampattern.

This paper designs a novel opportunistic adaptive non-orthogonal multiple access (OA-NOMA) strategy, where a base station (BS) employs NOMA to serve a near user (NU)-far user (FU) pair opportunistically scheduled from M NUs and K FUs. In particular, the NOMA transmission to the scheduled NU-FU pair adaptively operates in one of two modes: Direct NOMA mode, in which the BS directly serves the scheduled NU-FU pair with using NOMA; Cooperative NOMA mode, in which the scheduled NU receives the messages intended by both scheduled users from the BS, and then forwards the message intended by the scheduled FU. For the OA-NOMA strategy, a scheduling candidate acquisition method and a probabilistic user pair scheduling scheme are proposed to guarantee the transmission reliability and improve the scheduling fairness, respectively. To evaluate the scheduling fairness, we develop a max-min fairness criterion and show that the OA-NOMA strategy approximately achieves max-min fairness. The reliability of the OA-NOMA strategy is also evaluated in terms of outage probability and diversity order. For the outage probability, we derive an approximate expression and numerically verify its tightness. For the diversity order, we show that the proposed OA-NOMA strategy achieves a diversity order of M.

A cooperative jamming and beamforming scheme is proposed for secrecy transmission. Different from the existing works, an internal eavesdropper (Eve) and multiple external Eves coexist in the system, where a multi-antenna jammer is deployed to confuse Eves by using artificial noise. Specifically, the jammer can obtain the instantaneous channel state information (CSI) of the internal Eve, but only has the partial CSI of the external Eves. Upon setting up, the transmission outage requirement at Bob and secrecy outage constraint at the Eves, the transmit power is optimized to derive the maximum secrecy rate for the transmission. Moreover, we investigate the optimal power allocation scheme by characterizing the beamforming vectors under considering both perfect and imperfect CSI cases for the internal Eve. Numerical results show that the proposed scheme can improve the secrecy rate significantly by making full use of instantaneous CSI between the jammer and the internal Eve.

In this paper, we investigate spectrum sharing ultra- reliable and low- latency communications (URRLC) in an un- manned aerial vehicle (UAV)-aided cognitive radio (CR) internet of thing (IoT) network. Particularly, the secondary IoT devices opportunistically accesses the radio resource provided by a primary network and directly transmits short packets to the mobile UAV. A novel performance metric is proposed with finite block-length codes is adopted in the secondary UAV-aided IoT network. We aim to maximize the minimum average finite block-length rate for the secondary UAV-aided IoT network, subject to a probabilistic interference power constraint to the primary network based on imperfect channel state information (CSI). This formulated problem is non-convex due to the binary time scheduling, the power allocation, and the UAV altitude. In order to circumvent this issue, we develop an alternating method to solve this problem. Specifically, we first exploit the time scheduling optimization of the IoT devices for given power allocation and UAV altitude. Next, the monotonicity of the average finite block-length rate is analyzed to gain more insights for given time scheduling and UAV altitude. By capitalizing on this property, an optimal power control policy is proposed, followed by closed-form expressions and approximations for the optimal average power and the achievable average rate in the finite block- length regime. The optimal altitude of the UAV can be obtained by one-dimensional line search. Numerical results validate the effectiveness and accuracy of the derived theoretical results.

This paper investigates the sensor selection problem for time difference of arrival (TDOA) localization in wireless sensor networks. Specifically, a multi-objective optimization problem is formulated in which a Boolean vector is involved to find the best tradeoff between the localization accuracy and the energy consumption. The ε- constraints method is introduced to convert the original multi- objective optimization problem to a tractable single-objective problem. To solve the converted sensor selection problem, we propose the polyblock outer approximation (POA) algorithm based on discrete monotonic optimization (DMO) in order to find the global optimal solution, which however can not be obtained by the traditional semidefinite relaxation (SDR) approach. Further, for the sake of practical implementation, we propose another two suboptimal algorithms, namely, POA-based accelerated cutting (POA-AC) algorithm and POA-based monotonic cutting (POA-MC) algorithm. Simulation results validate that the localization accuracy for sensors selected by the POA-AC algorithm and POA-MC algorithm is greater than the semidefinite relaxation (SDR) solution and achieves the same results as that by the exhaustive search method.

This paper investigates the hybrid precoding design in millimeter-wave (mmWave) systems. To jointly consider the spectral efficiency and energy consumption, we propose an adaptive hybrid precoding structure, where a switch-controlled connection is deployed between every antenna and every radio frequency (RF) chain. To maximally enhance the spectral-and-energy efficiency under this structure, the joint optimization of the on-off states for switch-controlled connections and the hybrid precoding matrices is formulated as a non-convex problem. To efficiently solve this problem, we first propose an alternative limited Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) based algorithm to determine the hybrid precoders. Then, using the alternative L-BFGS algorithm, a greedy algorithm is proposed to jointly optimize the hybrid precoders and the on-off states of switch-controlled connections. It is theoretically shown that, this alternative L-BFGS based algorithm always converges to a stationary point. Further, the convergence of proposed greedy algorithm is also theoretically proved and validated by simulations. Simulation results also demonstrate that the proposed hybrid precoding achieves a superior tradeoff between the spectral efficiency and energy consumption.

This paper investigates the resource allocation in millimeter wave ultra dense networks, in which the beam assignment and sub-band allocation are jointly considered. Motivating to maximize the sum rate of the network conceived, the optimization problem is formulated as a mixed integer non-linear programing (MINLP) problem, which involves allocating the novel three-dimensional resource blocks (RBs) defined in beam (B), time (T), and frequency (F) dimension, respectively. To tackle the formulated MINLP problem, we propose the low-complexity resource allocation scheme, including the so-called best option first (BOF) beam assignment algorithm, and the many-to-one matching with externalities (M2O-ME) sub-band allocation algorithm. In particular, the BOF beam assignment algorithm is first carried out to coordinate the RBs in terms of T- and B-dimension. Then, with the aid of the mechanism of many-to-one with externalities, the M2O-ME sub-band algorithm is implemented to find the optimal sub-band allocation (i.e. RB allocation in F-dimension) solution. Finally, our simulation results show that the proposed resource allocation scheme can significantly outperform the existing schemes in terms of sum rate of the networks. Therefore, we can conclude that the proposed resource allocation scheme can be considered as a promising candidate for practical ultra dense small-cell networks with mmWave capability.

By blending the concepts of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiplexing, in this paper, a novel hybrid scheme is conceived for supporting diverse services in future wireless systems. Motivating to maximize energy efficiency (EE), the joint resource management of user clustering (UC) and power allocation is investigated for the downlink hybrid NOMA systems. Under two different power consumption cases, the optimal resource allocation (Opt-RA) algorithm is developed with the help of converting the original mixed integer non-linear programming (MINLP) problem to the tractable decoupled problems. For practical implementation, the heuristic resource allocation (Heur-RA) algorithm is also proposed, and it includes a low-complexity UC algorithm based on the candidate search-and-allocation approach. Our simulation results show that, both the Opt-RA and Heur-RA algorithms achieve significantly higher EE performance than other existing algorithms. Further, the results also prove that, the hybrid NOMA conceived is able to exploit the advantages of NOMA scheme, and is superior to conventional orthogonal multiple access (OMA) in terms of EE, as well as achieving higher flexibility for system configuration than NOMA.

A tractable stochastic geometry model is proposed to characterize the performance of novel point-to-multipoint (P2MP) assisted backhaul networks with millimeter-wave (mm-wave) capability. The novel performance analysis is studied based on the general backhaul network (GBN) and the simplified backhaul network (SBN) models. To analyze the signal-to-interference-plus-noise ratio (SINR) coverage probability of the backhaul networks, a range of the exact- and closed-form expressions are derived for both the GBN and SBN models. With the aid of the tractable model, the optimal power control algorithm is proposed for maximizing the trade-off between energy-efficiency (EE) and area spectral-efficiency (ASE) for the mm-wave backhaul networks. The analytical results of the SINR coverage probability are validated, and they match those obtained from Monte-Carlo experiments. The numerical results of the ASE performance demonstrate the significant effectiveness of our P2MP architecture over the traditional point-to-point setup. Moreover, our P2MP mm-wave backhaul networks are able to achieve dramatically higher rate performance than that obtained by the ultra-high-frequency networks. Furthermore, to achieve optimal EE and ASE tradeoff, the mm-wave backhaul networks should be designed to limit the link distances and line-of-sight interferences while optimizing the transmission power.

Next location prediction plays an essential role in location-based applications. Many works have been employed to predict the next location of an object (e.g. a vehicle), given its historical location records. However, existing methods have not fully addressed the importance of contextual features, such as the short-term traffic flows. In this paper, we propose a deep learning-based model to incorporate contextual features into next location prediction. First, we conduct the similarity mining among candidate locations. Second, we model contextual features among trajectories, including both periodical patterns and dynamic features of trajectories. Third, we adopt both CNN and bidirectional LSTM networks to predict next location in each trajectory with contextual information. Intensive experiments on 197 million vehicle license plate recognition (VLPR) records in Xiamen, China, demonstrate that the proposed method outperforms several existing methods.

This paper proposes a novel design of cooperative non-orthogonal multiple access (NOMA) for layered multicast, where the information is encoded into the messages of high-priority (HP) and low-priority (LP). Two types of multicast users coexist in the system: 1) regular users (RUs), which locate far away from the base-station (BS) and demand only the HP message; 2) advanced users (AUs), which locate close to the BS and demand both HP and LP messages. To improve the reliability of layered multicast, an opportunistic cooperative NOMA multicast strategy is proposed, in which one successful AU is selected to forward both HP and LP messages. For the proposed strategy, we derive closed-form exact outage probabilities of AUs and RUs. By further carrying out the asymptotic analysis, the achieved diversity orders are shown to be not less than the number of AUs, i.e., full diversity is achieved. Finally, numerical results verify the theoretical analysis and demonstrate the superiority of the proposed strategy.

In this paper we study on a massive MIMO relay system with linear precoding under the conditions of imperfect channel state information at the transmitter (CSIT) and per-user channel transmit correlation. In our system the source-relay channels are massive multiple-input multiple-output (MIMO) ones and the relay-destination channels are massive multiple-input single-output (MISO) ones. Large random matrix theory (RMT) is used to derive a deterministic equivalent of the signal-to-interference-plus-noise ratio (SINR) at each user in massive MIMO amplify-forward and decode-forward (M-MIMO-ADF) relaying with regularized zero-forcing (RZF) precoding, as the number of transmit antennas and users M,K→∞ and M≫K. Simulation results show that the deterministic equivalent of the SINR at each user in M-MIMO-ADF relaying and the results of Theorem 1, Theorem 2 are accurate.

This paper investigates the allocation of resources, including subcarriers and spreading codes, as well as intercell interference (ICI) mitigation for multicell downlink multicarrier direct-sequence code division multiple-access systems, which aim to maximize the system's spectral efficiency (SE). The analytical benchmark scheme for resource allocation and ICI mitigation is derived by solving or closely solving a series of mixed integer non-convex optimization problems. Based on the optimization objectives the same as the benchmark scheme, we propose a novel distributed resource allocation assisted by ICI mitigation scheme referred to as resource allocation assisted by ICI mitigation (RAIM), which requires very low implementation complexity and demands little backhaul resource. Our RAIM algorithm is a fully distributed algorithm, which consists of the subcarrier allocation (SA) algorithm named RAIM-SA, spreading code allocation (CA) algorithm called RAIM-CA and the ICI mitigation algorithm termed RAIM-IM. The advantages of the RAIM are that its CA only requires limited binary ICI information of intracell channels, and it is able to make mitigation decisions without any knowledge of ICI information. Our simulation results show that the proposed RAIM scheme, with very low complexity required, achieves significantly better SE performance than other existing schemes, and its performance is very close to that obtained by the benchmark scheme.

This paper designs a novel non-orthogonal multiple access (NOMA) unicast-multicast system, where a number of unicast users (those who require different messages) and a group of multicast users (those who require identical message) share the same time/space/frequency resource. For the designed NOMA unicast-multicast system, an efficient two-phase cooperation strategy is proposed to improve the reliability of all users. In the first phase, the base station (BS) broadcasts a superposed message consisting of all users' information. In the second phase, a multicast user is selected to forward the information intended by unsuccessfully decoded unicast and/or multicast users. Moreover, the multicast user selection is investigated under two different power allocation (PA) approaches: 1) fixed PA (FPA), in which the PA coefficients for both the phases are predetermined, and 2) dynamic PA (DPA), in which the PA coefficients for the first phase are predetermined, while the PA coefficients for the second phase are dynamically determined based on instantaneous channel information. Under the FPA approach, a best user selection (BUS) scheme (called F-BUS) is proposed to minimize the outage probability. Under the DPA approach, the local optimal PA coefficients for the second phase are derived in closed form first. Based on the derived PA coefficients, a BUS scheme (called D-BUS) is then proposed for outage probability minimization. To verify the reliability of the proposed cooperation strategy with employing the BUS schemes, we theoretically analyze the outage probability as well as diversity orders. It is shown that the proposed cooperation strategy achieves diversity orders equal to the number of multicast users, indicating that the inherent diversity orders offered by the multicast users are fully exploited. Finally, simulation results are presented to validate the theoretical results and demonstrate the advantages of the proposed cooperation strategy and the BUS schemes.

This paper addresses the subcarrier allocation in downlink multicarrier direct-sequence code-division multiple access (MC DS-CDMA) systems, where one subcarrier may be assigned to several users who are then distinguished from each other by their unique direct-sequence spreading codes. We first analyze the advantages and shortcomings of some existing subcarrier-allocation algorithms in the context of the MC DS-CDMA. Then, we generalize the worst subcarrier avoiding (WSA) algorithm to a so-called worst case avoiding (WCA) algorithm, which achieves better performance than the WSA algorithm. Then, the WCA algorithm is further improved to a proposed worst case first (WCF) algorithm. Furthermore, we propose an iterative worst excluding (IWE) algorithm, which can be employed in conjunction with the WSA, WCA, and the WCF algorithms, forming the IWE-WSA, IWE-WCA, and the IWE-WCF subcarrier-allocation algorithms. The complexities of these algorithms are analyzed, showing that they are all low-complexity subcarrier-allocation algorithms. The error performance is investigated and compared, demonstrating that we can now be very close to the optimum performance attained by the high-complexity Hungarian algorithm.

This contribution investigates the achievable error performance of transmitter preprocessing in the downlink multicell multicarrier direct-sequence code-division multiple-access (MMC/DS-CDMA) systems employing both time (T)-domain and frequency (F)-domain spreading. Three types of multiuser transmitter preprocessing (MUTP) schemes are studied and compared, when assuming communications over frequency-selective Rayleigh fading channels. The first one is a single-cell minimum mean-square error MUTP (SMMSE-MUTP1), which only aims at suppressing the intracell interference (IntraCI). The second one is also a single-cell MMSE-MUTP (SMMSE-MUTP2), which tries to suppress both the IntraCI and intercell interference (InterCI). The final one is the multicell cooperative MMSE-MUTP (CMMSE-MUTP), which exploits the multicell diversity (or macro-diversity) for interference suppression and performance enhancement. Furthermore, power-allocation in these schemes is considered. Our studies show that the CMMSE-MUTP is capable of achieving the best error performance among the three schemes considered. However, it demands an extremely high complexity, as it requires information exchange among the base-stations (BSs) with the aid of a backhaul system. By contrast, when the number of mobile terminals (MTs) supported by each cell is not very high, the SMMSE-MUTP2, which does not require any intercell cooperation, can effectively mitigate both the IntraCI and the InterCI.

The error performance of a two-hop communication link (THCL) supported by a cluster of relay nodes (RNs) is investigated. The THCL includes one source node (SN) and one destination node (DN). The SN sends information to the DN via a cluster of RNs. At the RNs, signals received from the SN are processed based on one of the three relay processing schemes. The three relay processing schemes considered include the distributed relay processing, maximal-ratio combining (MRC)-assisted relay processing and the majority vote and equal-gain combining (MV-EGC) aided relay processing. For the MRC-assisted and MV-EGC aided relay processing schemes, information exchange among the RNs is supported by a local information exchange network, within which communicates are based on the principles of direct-sequence code-division multiple-access (DS-CDMA). In this contribution, the bit error rate (BER) performance of the THCLs is investigated and compared, when assuming that the first and second hops of the THCLs experience flat Rayleigh fading and that the communications within the local information exchange network conflict only Gaussian noise. We address the impact of the energy consumed for RNs' cooperation on the achievable BER performance of the THCLs. Our studies demonstrate that, when the energy spent for cooperation is taken into account, cooperation among the RNs may impose a big trade-off on the achievable BER performance of the THCLs.

A multiway relay direct-sequence code-division multiple-access (MR-DS-CDMA) system is proposed for exchanging information among a group of distributed mobile terminals (MTs). In order to implement information exchange, a MT having similar distances from the other MTs is chosen to act as a relay, which assists the whole group of MTs to achieve their information exchange within two time-slots. In our proposed MR-DS-CDMA system, the relay is operated in detection-and-forward (DF) relaying strategy. Signals at the relay and the MTs are detected in the principles of minimum mean-square error multiuser detection (MMSE-MUD) or of the receiver multiuser diversity assisted multi-stage MMSE MUD (RMD/MS-MMSE MUD). The error performance of the MR-DS-CDMA is investigated, when communicating over Nakagami-m fading channels. Our studies show that the MR-DS-CDMA employing the RMD/MS-MMSE MUD at both the relay and MTs constitutes a high-efficiency information exchange system. It has low delay, promising error performance and low-complexity. Furthermore, it is robust to system overloading, where the number of MTs supported is higher than the spreading factor of the DS-CDMA system.