The vehicle road cooperation system is designed to enable intelligent and collaborative communication between vehicles and vehicles, infrastructure, and pedestrians to reduce road accidents and improve road efficiency. As a critical component of this, vehicle-to-vehicle (V2V) communication is expected to achieve efficient information interaction between vehicles and support services, such as collision warning and operation assistance. However, due to the high mobility of vehicles and channel fading, V2V communication suffers from link instability, high latency, and low-resource utilization. To address these issues, this article proposes a high-reliability low-latency intelligent geographic routing (HRLLIGR) protocol based on the greedy perimeter stateless routing (GPSR) protocol to improve its performance in such highly dynamic networks. The main mechanisms of HRLLIGR include a reliable greedy forwarding algorithm based on the evaluation of link stability metrics, a low-latency area prediction forwarding algorithm for routing voids, and an intelligent routing update mechanism to reduce routing overhead. Simulation results suggest that the HRLLIGR protocol outperforms traditional routing protocols, such as ad-hoc on-demand distance vector (AODV), optimized link state routing (OLSR), and GPSR regarding reliability, latency, and overhead. Specifically, compared to the GPSR protocol, it achieves 13.7% improvement in packet delivery rate, 21.3% reduction in average end-to-end latency, and 15.1% decrease in routing overhead.

The next generation of industrial internet of things (IoT) dominated by unmanned aerial vehicle (UAV) relies on the coordinated operation of heterogeneous UAV-mounted transceiver cluster (HUTC) in constrained environments. However, the electromagnetic resources available to these transceivers deployed in crowded spaces are limited, and the resulting spectrum conflicts can easily lead to difficulties in aerial sensing, computing, and networking. Beyond interference from external sources, spectrum allocation in dense spaces is further complicated by interference from frequency-domain neighbors, making efficient resource allocation challenging. Thus, this article utilize the electromagnetic interference (EMI) characteristics of heterogeneous transceivers as prior knowledge and proposes an innovative joint spectrum and power allocation based on better response (JSPA-BR) method to tackle the EMI problem in HUTC composed of heterogeneous transceivers. More specifically, we construct a game-theoretic model for joint spectrum and power allocation, and it is proved that the model constitutes an exact potential game (EPG) with at least one Nash equilibrium (NE) point. We then design the JSPA-BR algorithm which can converge quickly and approach the global optimal solution. Simulations and measurements show that this method maximizes the use of limited spectrum resources. It also mitigates EMI between transceivers and the external radiation of the system. It achieves electromagnetic compatibility of HUTC, thereby demonstrating the effectiveness and accuracy of the proposed approach.

Existing deep learning-based circuit design methods mostly focused on the primary matching of the model itself or circuit data, lacking generalizability and ignoring deep representation of electromagnetic coupling effects in coupled circuits. Therefore, it exhibits difficulties to further improve the accuracy in circuit performance prediction and requires large training datasets. To address these challenges, this article proposes an intelligent circuit design methodology with graph transfer learning (CircuitGTL). Specifically, it achieves the weighted graph modeling of complex electromagnetic environment circuits, where nodes represent components, edges represent the electromagnetic coupling effect between components, and edge weights signify the differential strength of electromagnetic coupling. Hereby, a fused graph representation model, integrating graph isomorphic network and electromagnetic coupling effect-based graph attention network, is proposed to achieve deep representation learning of graphic circuit data. Then, a model- and data-driven graph transfer learning mechanism considering joint optimizing of circuit’s performance matrix and nonperformance indicators is proposed. This is to achieve lightweight cross-electromagnetic topologies parameters optimization. Taking Terahertz (THz) resonant filters as an example to verify the effectiveness of CircuitGTL, numerical results on the MITCircuitGNN experimental dataset show that: compared with state-of-the-art algorithm CircuitGNN, CircuitGTL achieves 9.2% improvement in cross-electromagnetic topologies performance prediction accuracy, 90.9% reduction in model convergence time and 33.6% reduction in the total coverage area of components with only 20% of data requirement; additionally, the design of CircuitGTL has lower-insertion loss, steeper skirts, and higher-passband intersection-over-union. These results provide valuable insights for lightweight, and high-precision design of coupled electromagnetic structures.

With the advancement of Big Data technology, deep learning automatic modulation recognition (DLAMR) has undergone new improvements. Existing DLAMR methods focus mostly on the primary matching of the model itself or ubiquitous big communications data, which lack interpretability and ignore deep representations for the modulation mechanism of the communication signals; thus, difficulties in further improving the recognition accuracy and multiquadrant amplitude modulation (MQAM) discriminability in complex communication environments are encountered. In response to these challenges, this article proposes an innovative communication signal graph mapping method to address the uncertainty in the modulation mechanisms. Specifically, it models sampling points as nodes; connects inter- and intrasymbol points with edges to represent modulation mechanisms and propagation uncertainty; and maps amplitude, phase, in-phase, and quadrature values as node features. A deep graphical and temporal neuro-fuzzy methodology (GT-DNFS) that integrates graph attention networks and bidirectional long short-term memory networks is subsequently proposed for DLAMR. The numerical results show that GT-DNFS achieves a significantly higher recognition accuracy of 93.01%, and an MQAM (M=16, 64) discrimination of 94.5%. This research offers valuable insights for neuro-fuzzy networks and efficient DLAMR algorithm design.

Massive machine type communications (mMTC) is one of main application cases in 5G, which is supposed to support communications of massive number of machine-type devices (MTDs). Distributed queue (DQ) is a variant of tree splitting protocol which combines an m-ary tree splitting algorithm with a set of simple smart rules, organizing every terminal in one out of two virtual queues. Theoretically, DQ allows access to infinite terminals and is stable under any traffic condition, which alleviates the unstable problem of slotted ALOHA, and is especially suitable for mMTC. However, its theoretical comprehensive performance analysis as well as related statistical characteristics is still missing, which severely restricts the full manifestation of its performance advantages. In view of this, the paper proposes a general performance analysis framework for DQ, with which full probability space of DQ evolution process is presented for the first time. To be more specific, probability distribution function (PDF), mean and variance of throughput, latency and energy consumption of DQ is analytically derived to comprehensively evaluate performance. Taking the IEEE 802.15.4 standard for mMTC as example, numerical results validate the accuracy of the proposed analysis framework and the stability of DQ, present effects of number of MTDs, number of contention slots (${m}$), and maximum number of transmissions (${L}$) on DQ in terms of aforementioned performance metrics. These results together provide good reference to find appropriate value of${m}$and${L}$to balance the performance metrics and enable more practical network optimization.

Optical spatial modulation (OSM) and orthogonal frequency division multiplexing (OFDM) are two promising techniques for bandlimited intensity modulation/direct detection (IM/DD) optical wireless communication (OWC) systems. In this paper, we for the first time propose a novel enhanced OFDM-based OSM scheme for spectral efficiency improvement of ban-dlimited IM/DD OWC systems. The proposed enhanced OFDM-based OSM scheme can be considered as the combination of time-domain OSM (TD-OSM) and non-Hermitian symmetry OFDM (NHS-OFDM). In an OWC system adopting enhanced OFDM-based OSM, a pair of light-emitting diode (LED) transmitters are selected from the LED array, which are used to separately transmit the real and imaginary parts of a complex-valued NHS-OFDM signal. A modified maximum-likelihood (ML) detector is further developed to efficiently estimate the indexes of the LED pair and the real and imaginary parts of the transmitted complex-valued NHS-OFDM signal. We show that the proposed enhanced OFDM-based OSM scheme can achieve substantially improved spectral efficiency with moderate inter-channel interference and low transceiver complexity. Simulation results clearly verify the superiority of the proposed enhanced OFDM-based OSM scheme over the existing OFDM-based OSM schemes.

In this paper, we consider a light fidelity (LiFi)-enabled bidirectional Internet of Things (IoT) communication system, where visible light and infrared light are used in the downlink and uplink, respectively. In order to efficiently improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of both downlink and uplink channels. We derive closed-form OPA sets based on the identification of the optimal decoding orders in both downlink and uplink channels, which can enable low-complexity power allocation. Moreover, we propose an adaptive channel and QoS-based user pairing approach by jointly considering users' channel gains and QoS requirements. We further analyze the EE and the user outage probability (UOP) performance of both downlink and uplink channels in the bidirectional LiFi-IoT system. Extensive analytical and simulation results demonstrate the superiority of NOMA with OPA in comparison to orthogonal multiple access (OMA) and NOMA with typical channel-based power allocation strategies. It is also shown that the proposed adaptive channel and QoS-based user pairing approach greatly outperforms individual channel/QoS-based approaches, especially when users have diverse QoS requirements.

In this article, we propose an energy-efficiency-aware integrated architecture of cooperative computing (CC) to support the demands of computing amount in the blockchain-based Internet of Things (IoT). Specifically, we assume that multiple computing servers are placed at each data access point (DAP). The computing servers across multiple DAPs can be virtualized to constitute a CC pool to flexibly allocate the computing resource. When the amount of received data from a DAP is accumulated to a certain length of one data block, blockchain computing will be implemented to generate a correct Nonce value meeting the threshold of hash value. After the correct Nonce has been generated, the data block will be transmitted and stored in cloud caches, where the hash value is written into blockchain to guarantee the security of data block. We maximize system energy efficiency defined by the overall power consumption per unit of throughput transmitted from DAPs to cloud caches. We formulate the system optimization model by considering the constraints of data delay to avoid data overflow in the system. In order to solve the optimization model for maximizing system energy efficiency, we employ a geometric programming method to obtain the optimal power and resource allocation in blockchain-based IoT. By extensive simulations, we verify the effectiveness of our proposed energy-efficiency-aware optimization mechanism in the blockchain-based IoT.

Multi-carrier based non-orthogonal multiple access (NOMA) is an effective method to meet the ever-increasing demands of both user throughput and energy efficiency by multiplexing multiple users on the same carrier. Since interference from users with a poorer channel gain can be canceled at a user with a strong channel gain by successive interference cancellation, NOMA can enhance the system performance. To improve the downlink system performance, it is crucial to appropriately determine users scheduled on each carrier and power allocation at the base station. However, the existing works are generally either heuristic or local optimal due to the mixed optimization problem. In this paper, we focus on the global optimal solutions to maximize user throughput and energy efficiency in NOMA, respectively. In particular, we first formulate the mixed integer optimization problem which are intractable to be solved. Fortunately, by the provided analytical results, the optimization models can be largely simplified. Then, we propose the architectures of joint user scheduling and power allocation in NOMA, as well as the corresponding optimal algorithms. Simulation results demonstrate that our proposed algorithms indeed outperform existing works in terms of the user throughput and energy efficiency, respectively.

An innovative iterative process is proposed to acquire the dynamic process of multichannel slotted ALOHA (S-ALOHA). It reveals the direct relation between the number of contending devices that perform their jth random access (RA) attempt at the ith RA slot and the newly arrived devices before the ith RA slot. These results allow engineers to analytically derive the probability density function of RA delay of multichannel S-ALOHA, as well as its cumulative density function and average value. Under stable RA attempts assumption, simplified form of the above analysis is given, with which we prove the number of preamble transmissions follows truncated geometric distribution. Taking the two traffic models proposed for machine type communications as examples, numerical results are presented to verify the effectiveness of the proposed iterative process and the accuracy of its simplified form, and illustrate the delay characteristics of simplified long term evolution RA channel.