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.

The proliferation of unmanned aerial vehicles (UAVs) in low-altitude airspace necessitates sophisticated resource management supporting both cooperative communications and unauthorized intrusion detection. This paper investigates joint optimization of cell association and power allocation in integrated sensing and communication (ISAC)-enabled low-altitude networks. We propose a novel dual-function framework where ground base stations simultaneously provide communication services to authorized UAVs and localize non-cooperative UAVs for collision avoidance. We establish a channel model capturing the relationship between communication rate and sensing accuracy, formulating an optimization problem that maximizes the weighted sum of system average sum rate and localization quality of service (QoS). The problem jointly optimizes cell association, communication power allocation, and sensing power allocation under UAV localization QoS and cooperative sum rate constraints. To solve the resulting mixed-integer non-convex problem, we propose a joint optimization algorithm based on optimal transport theory (J2OT) that directly handles discrete variables without relaxation, avoiding accuracy losses of conventional approximation methods. J2OT decomposes the problem using optimal transport-based cell association optimization (OTC) and power allocation optimization (OTP). Simulation results demonstrate J2OT’s superiority, achieving 1.5 bits/s/Hz improvement in system objective and 7.5% reduction in localization Cramér-Rao bound compared to Weighted Voronoi and Iterative Water-filling baseline methods.

In this paper, we consider transmit beamforming and reflection patterns design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems, where the dual-function base station (DFBS) lacks channel state information (CSI). To address the high overhead of cascaded channel estimation, we propose an improved artificial fish swarm algorithm (AFSA) combined with a feedback-based joint active and passive beam training scheme. In this approach, we consider the interference caused by multipath user echo signals on target detection and propose a beamforming design method that balances both communication and sensing performance. Numerical simulations show that the proposed AFSA outperforms other optimization algorithms, particularly in its robustness against echo interference under different communication signal-to-noise ratio (SNR) constraints.

Low peak-to-average power ratio (PAPR) transmission is an important and favorable requirement prevalent in radar and communication systems, especially in transmission links integrated with high power amplifiers. Meanwhile, motivated by the advantages of reconfigurable intelligent surface (RIS) in mitigating multi-user interference (MUI) to enhance the communication rate, this paper investigates the design problem of joint waveform and passive beamforming with PAPR constraint for integrated sensing and communication (ISAC) systems, where RIS is deployed for downlink communication. We first construct a trade-off optimization problem for the MUI and beampattern similarity under PAPR constraint. Then, in order to solve this multivariate problem, an iterative optimization algorithm based on alternating direction method of multipliers (ADMM) and manifold optimization is proposed. Finally, the simulation results show that the designed waveforms can well satisfy the PAPR requirement of the ISAC systems and achieve a trade-off between radar and communication performance. Under high signal-to-noise ratio (SNR) conditions, compared to systems without RIS, RIS-aided ISAC systems have a performance improvement of about 50 % in communication rate and at least 1 dB in beampatterning error.

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.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments is critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution signal transformer (CST). The CST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel signal-specific self-attention mechanism to replace the multi-headed self-attention mechanism in Transformer, and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The simulation results demonstrate that the CST outperforms advanced neural networks on all datasets, which is very beneficial for the deployment of AMR in complicated channel environments.

With the development of sixth generation (6G) networks, semantic communication is treated as an emerging technology to provide more intelligent communication between two parties for enhance the accuracy and efficiency of communications. Whereas, it demands to merge all physical layer blocks in the conventional communications, which heavily deteriorates the spectrum sparsity problem of wireless communication networks. For this sake, we develop a novel intelligent relay assisted semantic communications, by enhancing communication efficiency, which creates a paradigm for text transmission fusion in 6G networks. In this contribution, the semantic communication system we have designed combines traditional deep learning methods with intelligent semantic relays, based on which, the protocols about minimizing the semantic errors by recovering the meaning of sentences for addressing channel variations issues. Meanwhile, our designed system could be applied in the case of wireless channel deterioration or knowledge background mismatch at the transmitter and receiver, and is also a paradigm for future applications in one-to-many and many-to-many semantic communication. At last, we propose a range of research directions and open challenges for boosting the implementations of relay assisted semantic communications.

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.

Due to the high flexibility and mobility, unmanned aerial vehicles (UAVs) can be deployed as aerial relays touring to disseminate data to ground users (GUs), especially when the ground base station is temporally dysfunctional or damaged. However, the broadcasting nature of wireless communication leads to the security issue with the presence of malicious eavesdroppers (Eves). The paper proposes a matching combined multi-agent deep reinforcement learning (DRL) to maximize the average secure rate for UAV-ground communications in probabilistic line-of-sight (LoS) channels, with the joint consideration of UAVs' propulsion energy and trajectory, as well as GUs' dissemination data size. The numerical simulation demonstrates that comparing with the other no matching combined DRL or valued-based DRL approaches, the proposed matching combined multi-agent deep deterministic policy gradient (matching-MADDPG) has better performance at both trajectory and convergence.

In multi-UAV assisted offloading environment, to enhance the offloading transmission rate and energy efficiency (EE) with limitation of pre-assigned docking station for UAV and users' of-floading task size, UAVs' service assignment with users, users' transmit power and time-scheduling, as well as UAVs' trajectory should be jointly optimized. However, these dynamics factors are inter-evolved with each other, especially the service assignment among UAVs and users is indicated by the binary vector that makes aforementioned issue be a mixed integer non-convex optimization that is challenge to be solved. This paper proposes a clustering-combined successive convex approximation (SCA) approach to maximize the users' EE with jointly optimized the service assignment, users' transmission power as well as UAVs' trajectory. Different from the approaches that simple relax the binary service assignment indication in to the consecutive space, and then take block coordinate descent (BCD) technique for energy and trajectory optimization, the proposed clustering-combined SCA can effectively optimize UAVs' trajectory as well as users' offloading scheduling in multi-UAV scenario. The numeral simulation reveals which outperform those relaxation method with respect to users' offloading EE and UAV's trajectory.

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 resource allocation and vehicle to everything (V2X) offloading in the cognitive vehicular networks. The cognitive radio (CR), mobile edge computing (MEC), and non-orthogonal multiple access (NOMA) schemes are applied aim to solve the combinational problem of resource allocation and V2X offloading. The problem for jointly optimizing power and time allocation in the MEC based CR (CR-MEC) networks is conceived. We decompose the joint optimization problem into two subproblems, which are power allocation and time allocation problems. In order to solve this joint optimization problem, an advanced comprehensive resource allocation (ACRA) algorithm based on three-sided matching theory is employed. More specifically, the proposed algorithm is to realize the most reasonable matching among primary users (PUs), cognitive users (CUs) as well as a cognitive base station (BS), and put forward a V2X offloading strategy, by appropriately allocating power and time aim to minimize the system energy consumption. The simulation results show that, our proposed algorithm converges to stable. Furthermore, the proposed NOMA based CR-MEC networks can achieve lower energy consumption compared to the orthogonal multiple access (OMA) based CR-MEC networks.

Buffer-aided relay technique can improve the diversity order and offer secrecy provision. To further improve secrecy performance, this paper proposes a secure link selection for relay networks where a new link selection policy is first designed under the constraint on the buffers and channel states using a Markov chain. The stationary state and the corresponding state transition matrix can be derived, and they are used to analyze the secrecy performance. Through the derivation of secrecy outage probability, we can get its closed-form expressions. Numerical results demonstrate that the proposed secure transmission scheme has a better performance than the conventional buffer-aided secure transmission schemes in terms of secrecy outage probability.

Internet of Things (IoT) technology suffers from the challenge that rare wireless network resources are difficult to meet the influx of a huge number of terminal devices. Cache-enabled device-to-device (D2D) communication technology is expected to relieve network pressure with the fact that the requesting contents can be easily obtained from nearby users. However, how to design an effective caching policy becomes very challenging due to the limited content storage capacity and the uncertainty of user mobility pattern. In this article, we study the jointly cache content placement and delivery policy for the cache-enabled D2D networks. Specifically, two potential recurrent neural network approaches [the echo state network (ESN) and the long short-term memory (LSTM) network] are employed to predict users' mobility and content popularity, so as to determine which content to cache and where to cache. When the local cache of the user cannot satisfy its own request, the user may consider establishing a D2D link with the neighboring user to implement the content delivery. In order to decide which user will be selected to establish the D2D link, we propose the novel schemes based on deep reinforcement learning to implement the dynamic decision making and optimization of the content delivery problems, aiming at improving the quality of experience of overall caching system. The simulation results suggest that the cache hit ratio of the system can be well improved by the proposed content placement strategy, and the proposed content delivery approaches can effectively reduce the request content delivery delay 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.

In recent years, unmanned aerial vehicles (UAVs) are more widely applied due to low cost and high flexibility. Facing the suddenness of emergency events and the uncertainty of user service requests, the rational deployment of the UAV aerial base stations (ABSs) has become an effective solution. However, how to deploy multiple UAVs with the variety of properties (power, service radius, and so on) is a challenge problem. There is a mean-field-type game (MFTG) to obtian optimal startegies which has been applied in practical applications. The arbitrariness of the number, the distinguishability of agents and the non-negligible effect on system are considered in MFTG. Because the homogeneity of multiple UAVs cannot be guaranteed in the practical communication model, this paper formulates the deployment of multiple UAV ABSs with various properties as a MFTG. In this scenario, each UAV decides the flight strategy at the next moment to minimize its own cost function by analyzing the communication requests of the ground users. In addition, the existence of the Nash equilibrium of the MFTG problem is proved. And the direct square complement method is used to solve the problem to minimize the cost function of each UAV. The simulation results show the correctness of the proposed method and the rationality of the deployment.

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.

In this paper, we study the user pairing in a downlink non-orthogonal multiple access (NOMA) network, where the base station allocates the power to the pairwise users within the cluster. In the considered NOMA network, a user with poor channel condition is paired with a user with good channel condition, when both their rate requirements are satisfied. Specifically, the quality of service for weak users can be guaranteed, since the transmit power allocated to strong users is constrained following the concept of cognitive radio. A distributed matching algorithm is proposed in the downlink NOMA network, aiming to optimize the user pairing and power allocation between weak users and strong users, subject to the users' targeted rate requirements. Our results show that the proposed algorithm outperforms the conventional orthogonal multiple access scheme and approaches the performance of the centralized algorithm, despite its low complexity. In order to improve the system's throughput, we design a practical adaptive turbo trellis coded modulation scheme for the considered network, which adaptively adjusts the code rate and the modulation mode based on the instantaneous channel conditions. The joint design work leads to significant mutual benefits for all the users as well as the improved system throughput.

An uplink transmission for Adaptive Dynamic Network Coding(ADNC) assisted Cooperative Cognitive Radio(CCR) system is proposed for facilitating the recovery of the source information received from the Primary Users (PUs) at the BS. The Cognitive Users (CUs) acting as Relay Nodes invoke the ADNC technique, where the CCR- based control information is exchanged between the CUs and the BS. % The network encoder may be activated in its adaptive mode for the sake of supporting the CUs, depending on the Boolean value of the feedback flags generated by the receiver based on the success/failure of the Adaptive Turbo Trellis Coded Modulation (ATTCM) channel decoder and of the network decoder. As a result, our novel ATTCM-ADNC-CCR system constructed based on a holistic approach is capable of providing an increased throughput, despite reducing the transmission-period of the PU. This reduced transmission-period can also be directly translated into an increased time-duration for the secondary communications of the CUs.

A pragmatic distributed algorithm (PDA) is proposed for supporting the efficient spectral access of multiple Primary Users (PUs) and Cognitive Users (CUs) in cooperative Cognitive Radio (CR) networks. The CUs may serve as relay nodes for relaying the signal received from the PUs to their destinations, while both the PUs' and the CUs' minimum rate requirements are satisfied. The key idea of our PDA is that the PUs negotiate with the CUs concerning the specific amount of relaying and transmission time, whilst reducing the required transmission power or increasing the transmission rate of the PU. Our results show that the cooperative spectral access based on our PDA reaches an equilibrium, when it is repeated for a sufficiently long duration. These benefits are achieved, because the PUs are motivated to cooperate by the incentive of achieving a higher PU rate, whilst non-cooperation can be discouraged with the aid of a limited-duration punishment.

A pragmatic distributed algorithm (PDA) is proposed for supporting the efficient spectral access of multiple Primary Users (PUs) and Cognitive Users (CUs) in cooperative Cognitive Radio (CR) networks. The novelty of our PDA is that the PUs negotiate with the CUs concerning the specific amount of relaying and transmission time, the CU is granted, which the CU will either accept or decline. The CUs may serve as relay nodes for relaying the signal received from the PUs to their destinations, while both the PUs' and the CUs' minimum rate requirements are satisfied. This will reduce the required transmission power and/or increase the transmission rate of the PU. Our results show that the proposed scheme performs better than the benchmarker, despite its significantly lower overhead and complexity. Moreover, we show that the cooperative spectral access based on our PDA reaches an equilibrium, when it is repeated for a sufficiently long duration. These benefits are achieved, because the PUs are motivated to cooperate by the incentive of achieving a higher PU rate, whilst non-cooperation can be discouraged with the aid of a limited-duration punishment. Furthermore, we invoke an attractive practical adaptive Turbo Trellis Coded Modulation (ATTCM) scheme, which appropriately adjusts the code rate and the modulation mode according to the near-instantaneous channel conditions. It was found that the joint design of coding, modulation and user-cooperation may lead to significant mutual benefits for all the PUs and the CUs.

The active cooperation between a primary user (PU) and a cognitive user (CU) has the potential of leading to a transmission power reduction and transmission rate increase for both the PU and the CU. Alternatively, the required bandwidth may be reduced and the freed bandwidth may be leased to a group of CUs for their secondary communications. More explicitly, our cooperative protocol allows a CU to serve as a relay node (RN) for relaying the signal of the first PU, which is a source node (SN), to the second PU, which is a destination node (DN). Furthermore, we conceived adaptive turbo trellis coded modulation (ATTCM) for appropriately adjusting both the code rate and the modulation mode according I to the near‐instantaneous channel conditions. More specifically, we propose an ATTCM aided two‐way relaying cooperative CR scheme that maximises the CU's own data rate and improves the exploitation of the bandwidth released by the PUs. Our numerical and simulation results show that the bandwidth reduction attained by the proposed two‐way relay based CR scheme is more than 80% of the PU's bandwidth.

An adaptive Turbo Trellis Coded Modulation (ATTCM) aided Cognitive Radio (CR) scheme is proposed for cooperative communication among Primary Users (PUs) and Cognitive Users (CUs). The new cooperative protocol allows a CU to serve as a Relay Node (RN) for relaying the signal of the first PU, which is a Source Node (SN) to the second PU, which is a Destination Node (DN). More specifically, an active cooperation between the PU and the CU would lead to a reduction of the transmission power and/or to an increased transmission rate for the PU. These benefits may be translated into a reduced transmission bandwidth and the freed bandwidth may be leased to a group of CUs for their secondary communications. Furthermore, our ATTCM scheme appropriately adjusts the code rate and the modulation mode according to the near-instantaneous channel conditions. The ATTCM switching thresholds are chosen to ensure that the Bit Error Ratio (BER) is below 10-6in order to minimize the potential error propagation from the RN to the DN. It was found that the joint design of coding, modulation, user-cooperation and CR techniques may lead to significant mutual benefits for both the PUs and the CUs.

A novel power- and bandwidth-efficient Turbo Trellis Coded Modulation (TTCM) assisted Space Division Multiple Access (SDMA) based two-way relaying scheme is proposed. The scheme advocated was designed for enhancing the throughput, reliability and coverage area in a cooperative communication system. A twin-antenna Relay Node (RN) is employed for assisting a pair of users, where each user is equipped with a single-antenna mobile unit. During the first transmission period, both users transmit their TTCM-encoded signals to the RN. The twin-antenna RN then detects these signals using various SDMA-based detection algorithms. Iterative SDMA and TTCM detection is invoked at the RN, which then broadcasts the re-encoded TTCM signals to both users during the second transmission period. Finally, each user retrieves the opposite user's signals received from the RN. Our proposed scheme outperforms the non-cooperative TTCM scheme by approximately 5.3 dBs at a BER of 10-6, when communicating over uncorrelated Rayleigh fading channels.