This paper systematically analyzes and optimizes the outage performance for a multi-cell vehicle-to-infrastructure (V2I) network, wherein each cell is assisted by a roadside-mounted reconfigurable intelligent surface (RIS) for signal enhancement. We first derive the closed-form outage probability (OP) expression for Nakagami-m-fading V2I links by modeling signal and interference distributions via the moment-matching technique. The OP expression reveals that enlarging the element quantity of RISs can significantly reduce OP, as the inter-cell interference grows much slower than the signal power due to incoherent combination of interference paths. To further optimize the network-wide outage performance, we propose two power control mechanisms: a distributed game-based approach achieving Nash equilibrium through properly-designed utility functions, and a centralized deep reinforcement learning (DRL)-based approach that minimizes the maximum OP of all the V2I links by leveraging the advanced soft actor-critic (SAC) algorithm. Finally, simulations verify our theoretical derivations, and demonstrate that RIS deployment combined with optimized power control substantially improves the reliability of V2I networks.

In industrial internet of things (IIoT) scenarios, the energy efficiency of task offloading is challenged by the quasi-periodic fading of wireless channels and the energy constraints of IIoT devices. To address this, we propose a multi-stage offloading framework, which allows intelligent reflecting surfaces (IRS)-assisted unmanned aerial vehicles (UAV) to dynamically reflect transmitted signals between a small base station (SBS) and a macro base station (MBS), aiming to mitigate inter-tier and cross-tier interference. However, achieving efficient offloading while minimizing energy consumption remains a critical challenge due to the complex interplay between device offloading decisions, IRS phase shift design, subchannel allocation, and power control. To tackle this, we first formulate a mixed-integer nonlinear programming problem based on uplink communication and computational models. Then, an improved escape optimization algorithm (IESC) is developed to solve the problem, which achieves efficient convergence through dynamic solution space exploration. Finally, simulation results demonstrate that our proposed scheme significantly outperforms existing benchmarks in terms of energy efficiency and offloading performance.

This paper aims to reveal the potential of hybrid non-orthogonal multiple access (NOMA) in improving energy efficiency, which combines the advantages of NOMA and conventional orthogonal multiple access (OMA). In particular, a novel hybrid NOMA scheme is proposed, which can be implemented as an simple add-on to the legacy OMA network. Specifically, in the proposed hybrid NOMA scheme, a user can transmit signals by using not only its own allocated channel resource block as in OMA, but also sharing the channel of other users via NOMA. To release the potential of hybrid NOMA, a flexible successive interference cancellation (SIC) method is adopted. Rigorous analysis is provided, which indicates that even with less energy consumption, the proposed hybrid NOMA scheme offers a higher data rate than the conventional OMA scheme. The numerical results support the analysis and highlight the superior performance of HSIC in comparison to FSIC.

This paper aims to study the impact of non-orthogonal multiple access (NOMA) assisted grant-free transmissions on reducing age of information (AoI) in status updating systems. Particularly, an uplink communication scenario is considered, where multiple users upload their status updates to a destination competitively. To mitigate collisions among users,$K$reception signal-to-noise ratio (SNR) levels are pre-configured, which can be chosen randomly by the users. By applying NOMA, successive interference cancellation (SIC) is carried out at the receiver to decode the signals from different users sequentially. Different from most existing works which adopt generate-at-will (GAW) model for modeling the generating process of status updates' arrivals, this paper considers a more general model to characterize the randomness of the status updates' arrivals. Closed-form expressions for AoI achieved by the proposed NOMA assisted grant-free scheme is obtained. Numerical results are provided to validate the accuracy of the analytical results and also demonstrate the superior performance of the proposed scheme in reducing AoI compared to conventional OMA based methods.

Cognitive vehicle-to-vehicle (V2V) communication, operating as an underlay to vehicle-to-infrastructure (V2I) communication systems, serves as a critical enabler for platoon driving applications. In practice, ensuring reliable V2I links while maintaining high-throughput intra-platoon links is essential for road safety. This paper investigates the joint optimization of relay selection and resource allocation in a long platoon network, where a single relay vehicle facilitates intra-platoon connectivity. The objective is to maximize the long-term V2V sum-rate while satisfying V2I signal-to-interference-plus-noise ratio (SINR) constraints and relay service duration limitations. To solve this temporally coupled optimization problem with hybrid decision variables—involving discrete relay and channel selection as well as continuous power control—we adopt a Hybrid Proximal Policy Optimization (H-PPO) framework. The proposed solution employs dual actor networks to simultaneously learn optimal discrete and continuous policies. Extensive simulations validate that the H-PPO algorithm achieves substantial improvements in V2V throughput while guaranteeing V2I reliability and energy-efficient relay operation in high-mobility long-platoon scenarios.

This paper studies the application of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) to reduce age of information (AoI) for uplink transmission. In particular, a time division multiple access (TDMA) based legacy network is considered, where each user is allocated with a dedicated time slot to transmit its status update information. The CR-NOMA is implemented as an add-on to the TDMA legacy network, which enables each user to have more opportunities to transmit by sharing other user’s time slots. A rigorous analytical framework is developed to obtain the expressions for AoIs achieved by CR-NOMA with and without re-transmission, by taking the randomness of the status update generating process into consideration. Numerical results are presented to verify the accuracy of the developed analysis. It is shown that the AoI can be significantly reduced by applying CR-NOMA compared to TDMA.Moreover, the use of re-transmission is helpful to reduce AoI, especially when the status arrival rate is low.

In advanced vehicular networks, Roadside Unit (RSU)-based amplify-and-forward (AF) relay and passive Reconfigurable Intelligent Surface (RIS) are two potential helpers to enhance the vehicle-to-vehicle (V2V) communications when the direct link experiences poor quality. This paper presents a comprehensive comparison of the two enhancement modes from the outage performance perspective. In the presence of both direct link and enhanced link, the analytical expressions of the outage probability (OP) for the V2V communication under the two enhancement modes are derived respectively. Moreover, considering the co-channel interference caused by relay/RIS, the OP of the neighbouring vehicle-to-infrastructure (V2I) communication is also derived. Additional analysis compares the diversity order and the strength of interference created by the V2V communication under the two enhancement modes. Further discussions are presented on the effect of the channel estimation error and phase quantization error under the RIS mode. Finally, the pros and cons of the two enhancement modes are demonstrated by both the analytical and numerical results.

Hybrid non-orthogonal multiple access (NOMA), which organically combines pure NOMA and conventional OMA, has recently received significant attention to be a promising multiple access framework for future wireless communication networks. However, most of the literatures on hybrid NOMA only consider fixed order of successive interference cancellation (SIC), namely FSIC, for the NOMA transmission phase of hybrid NOMA, resulting in limited performance. Differently, this paper aims to reveal the potential of applying hybrid SIC (HSIC) to improve the energy efficiency of hybrid NOMA. Specifically, a HSIC aided hybrid NOMA scheme is proposed, which can be treated as a simple add-on to the legacy orthogonal multiple access (OMA) based network. The proposed scheme offers some users (termed “opportunistic users”) to have more chances to transmit by transparently sharing legacy users’ time slots. For a fair comparison, a power reducing coefficient$\beta $is introduced to ensure that the energy consumption of the proposed scheme is less than conventional OMA. Given$\beta $, the probability for the event that the achievable rate of the proposed HSIC aided hybrid NOMA scheme cannot outperform its OMA counterpart is obtained in closed-form, by considering impact of user pairing. Furthermore, asymptotic analysis shows that the aforementioned probability can approach zero in the high SNR regime under some given conditions, which are compositely determined by the users’ transmit powers, primary user’s target data rate and$\beta $, indicating that the energy efficiency of the proposed scheme is almost surely higher than that of OMA for these given conditions. Numerical results are presented to verify the analysis and also demonstrate the benefit of applying HSIC compared to FSIC.

This work studies the joint transmit power control and receive beamforming in uplink rate splitting multiple access (RSMA)-based low earth orbit (LEO) satellite networks, using both generative diffusion model and proximal policy optimization (PPO) learning framework. In particular, using RSMA, interference is partially decoded and partially treated as noise, thereby improving the spectral efficiency, while the dynamics and uncertainty in LEO satellite networks would pose challenges to the real-time power control and receive beamforming optimization. First, a long-run sum data rate maximization problem is formulated, subject to the individual data rate requirement, and then the Markov decision process (MDP) is used to model it. Second, on the basis of MDP, a generative diffusion model-based proximal policy optimization (PPO) framework is proposed, where a denoising network is taken as the actor network in PPO to output the optimal continuous policy, thereby facilitating the hyperparameter tuning and improve the sample efficiency. Finally, experiments are conducted to show advantages of merging diffusion model into PPO, in terms of larger spectral efficiency, by comparing proposed framework with benchmarks.

Vehicle-to-infrastructure (V2I) network is a new paradigm of wireless system with special topology where roadside units (RSUs) are linearly deployed along the roadside and vehicles linearly move on the road. For such system, some classical problems would have new formulations and solutions. We in this paper investigate the joint power control and user scheduling problem for a multi-cell downlink V2I network, the objective of which is to maximize the sum-rate of the network under the signal-to-interference-plus-noise ratio (SINR) constraint of each V2I link. Considering the high mobility of vehicles, the objective function is set as the mean of the sum-rate over a sequence of time slots. For ease of handling, we first decouple the problem into multiple separate subproblems based on the linear distribution of RSUs. Then we employ the quadratic transform technique for fractional programming (FP) to transform the mixed integer nonlinear programming (MINLP) subproblems into convex problems, and obtain the solutions with Branch and Bound method. Finally the validity of our proposed algorithm is verified by numerical simulations.

This paper aims to study the role of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) in improving information freshness of status updating systems with random arrivals. Particularly, a time slotted multiuser scenario is considered, where each user transmits its status update to a receiver. Each user is allocated with a time slot. In CR-NOMA, each user can transmit signal as a primary user by using its own time slot, besides, it can also transmit signal as a secondary user by sharing the time slot of another user. Hence, more transmission opportunities are provided by CR-NOMA compared to conventional time division multiple access (TDMA) based schemes. In this paper, age of information (AoI) is utilized as the performance metric. For comparison purpose, exact expressions for the average AoIs achieved by CR-NOMA and benchmark TDMA are obtained. Numerical results are provided to validate the accuracy of the analytical results and also demonstrate the superior performance of CR-NOMA on reducing AoI. It is shown that the AoI can be significantly reduced by CR-NOMA compared to TDMA.

The aim of this paper is to reveal the importance of hybrid successive interference cancellation (SIC) and power adaptation (PA) for improving transmission robustness of uplink non-orthogonal multiple access (NOMA). Particularly, a cognitive radio inspired uplink NOMA communication scenario is considered, where one primary user is allocated one dedicated resource block, while$M$secondary users compete with each other to be opportunistically served by using the same resource block of the primary user. Two novel schemes are proposed for the considered scenario, namely hybrid SIC with PA (HSIC-PA) scheme and fixed SIC with PA (FSIC-PA) scheme. Both schemes can ensure that the secondary users are served without degrading the transmission reliability of the primary user compared to conventional orthogonal multiple access (OMA) based schemes. The novelty of the proposed schemes compared to those existing schemes is the introduction of power adaptation. This paper presents rigorous analytical results to show that both schemes can avoid outage probability error floors without any constraints on users’ target rates in the high SNR regime, which cannot be achieved by the existing schemes. Furthermore, it is shown that the diversity gain achieved by the HSIC-PA scheme is$M$, while that of the FISC-PA scheme is only 1. Numerical results are provided to verify the developed analytical results and also demonstrate the superior performance achieved by the proposed schemes by comparing with the existing HSIC without PA (HSIC-NPA) scheme. The presented simulation results also show that HSIC-PA scheme performs the best among the three schemes, which indicates the importance of the combination of HSIC and PA for improving transmission robustness.

The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the precoding design for downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple antennas. In specific, under quasi-degraded channels, simple linear precoding based N-NOMA can achieve the same minimal total transmission power as theoretically optimal but complicated dirty paper coding (DPC) scheme, when the users’ target rates and minimal transmission power of each BS are given. In this paper, the channel quasi-degradation (QD) condition is first rigorously derived for the scenario with single CoMP user and two NOMA users. The closed-form optimal precoders for N-NOMA under quasi-degraded channels are also provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme is proposed, which is a mixture of N-NOMA and conventional zero-forcing beamforming (ZFBF) scheme. Further, for the scenarios with more users, a low-complexity QD based user pairing (QDUP) algorithm is proposed, which is then combined with H-N-NOMA to make a novel scheme termed H-N-NOMA/QDUP into being. Numerical results are presented to reveal the impact factors on QD channels, and also demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the benefit of multi user diversity.

The aim of this paper is to present a groundwork on the delay-minimized routing problem in a vehicular ad-hoc network (VANET) where some of the vehicles are equipped with full-duplex (FD) radios. We first give the generalized delay calculation model for a multi-hop path, and prove that the Dijkstra algorithm is unable to get the delay-minimized routing path from source to destination. Then we propose two routing methods: graph-based method and deep reinforcement learning (DRL)-based method. In the graph-based method, the network topology is reformulated as an equivalent graph and then an evolved-Dijkstra algorithm is proposed. In the DRL-based method, the deep Q network (DQN) is employed to learn the shortest end-to-end path, wherein the delay is modeled as the rewards for routing actions. The graph-based method can achieve the exact minimum end-to-end delay, while the DRL-based method is more feasible due to its acceptable complexity. Finally, extensive simulations demonstrate that the DRL-based approach with proper hyper-parameters can achieve near minimum end-to-end delay, and the achieved delay has a notably decline as the number of FD nodes increases.

Full-duplex (FD) radio, which enables co-time co-channel receiving and forwarding at one device, is expected to be leveraged to improve the relay efficiency of multi-hop inter-vehicle communications. This paper considers a vehicular ad-hoc network (VANET) with some of the vehicles being FD enabled. We first present the new calculation model of the end-to-end delay, and then prove that the Dijkstra algorithm is unable to get the shortest (i.e. delay-minimized) path from source to destination. To handle this, we reformulate the network topology as an equivalent graph through decoupling all the FD-related links, and then propose an evolved-Dijkstra algorithm to find the shortest routing path of the reformulated graph with low complexity. Extensive simulations demonstrate that our approach can achieve the minimum end-to-end delay of inter-vehicle communications, and the achieved delay has a notably decline as the number of FD nodes increases.

With the capability of bidirectional communications on a single frequency band, the full-duplex (FD) operation can potentially double the spectral efficiency in physical layer. In network layer, nevertheless, it may cause severe mutual interference to the system. In this paper, we exploit interference alignment (IA) to address the interference in small cell networks, where some of the base stations simultaneously serve both uplink and downlink users on the same frequency via FD. Under such scenario, we first derive the feasibility condition for IA from Bezout's theorem and find that IA can be feasible only if a certain size constraint of the network is satisfied. On this basis, we then propose two clustering methods, i.e., minimized spectrum consumption clustering (MSCC) and minimized interference leakage clustering (MILC), both of which can perfectly eliminate the intra-cluster interference with IA. The difference between them is that MSCC aims at minimizing the number of clusters through allocating orthogonal resource blocks (RBs) for each cluster to avert inter-cluster interference, while MILC tries to minimize the aggregated inter-cluster interference with all clusters sharing the same RB. Extensive simulations verify that MSCC can achieve higher system sum rate, but MILC works better in terms of spectral efficiency.