Physical Communication最新文献

This article proposes using in-phase and quadrature frequency-shift keying (IQFSK) modulation for low-power optical wireless communications (OWC). IQFSK independently leverages both cosine and sine basis functions to enhance the system’s spectral efficiency (SE). It uses only the odd harmonic frequencies for these basis functions, allowing the clipping of negative amplitude excursions without losing information, making the waveform compatible with OWC The work presents optimal maximum likelihood and low-complexity sub-optimal detection mechanisms for IQFSK. The proposed scheme is analyzed analytically and with numerical simulations. The simulation and analytical results indicate that the proposed scheme is more energy-efficient, can attain a better energy and SE trade-off by exploiting the frame structure of the waveform, and has a lower minimum squared Euclidean distance relative to other state-of-the-art FSK-based counterparts, thus establishing it as one of the most efficient FSK approaches for low-power OWCs.

As an essential technology in the sixth generation of wireless communication, the reconfigurable intelligent surface (RIS) offers transformative solutions for the evolution of intelligent transportation. In the secondary network, RIS is equipped on an unmanned aerial vehicle (UAV) to establish a communication link between secondary base station (SBS) and secondary mobile vehicles (SMVs). At the same time, the communication within the secondary network must not interfere with the primary users (PUs) in the primary network. To achieve the optimal energy efficiency, we need to optimize the RIS passive beamforming, SMVs communication scheduling, SBS radiation power allocation and RIS-UAV trajectory. Since the original problem is difficult to solve, we use an alternating iteration framework to decompose the original problem into four subproblems and solve them with successive convex approximations (SCA). We have developed the CEEM scheme to compare it with benchmark schemes and demonstrate its superior performance, achieving up to a 43.48% improvement. In addition, RIS improves the communication quality by up to 57.53% in the simulation results, which have verified the correctness and effectiveness of the algorithm proposed in this paper.

In recent years, deep learning (DL) has become one of the potential solutions for massive MIMO signal detection. Considering that eliminating interference among the receive antennas at the base-station is intrinsically critical, we propose a method that combines DL and interference cancellation (IC) algorithms for uplink signal detection in massive MIMO systems. Firstly, by optimizing the conventional detection network (DetNet) and the sparsely connected neural network (ScNet) detection algorithms, we propose an enhanced version of ScNet, named EScNet, based on the convolutional neural networks (CNN). Secondly, an IC mechanism is employed, and its corresponding DNN layer structure is designed accordingly. Specifically, parallel and successive interference cancellation-aided EScNet algorithms, namely EScNet-PIC and EScNet-SIC, are proposed, respectively. The proposed algorithms are implemented with two stages on each DNN layer, where the first stage accounts for the proposed EScNet algorithm, which demodulates the received symbols as the input to the second stage for interference cancellation. Simulation results verify that our proposed EScNet-PIC and EScNet-SIC algorithms are particularly salient for massive MIMO signal detection compared to various existing algorithms, and they achieve an SNR gain of at least 0.5 dB at the BER level of $10^{-3}$ and up to 4dB for various antenna configurations. Moreover, the proposed algorithms also exhibit fast and stable convergence and relatively low complexity. With the capability of operating in both independent and correlated fading channel environments, they can serve as promising technical candidates for massive MIMO signal detection.

Energy harvesting (EH)-assisted non-orthogonal multiple access (NOMA) cognitive radio (CR) networks allow simultaneous transmission of multiple secondary user signals on primary frequency bands with harvested energy, enhancing spectral, spectrum utilization, and energy efficiencies. Although multiple antennas are used for efficient energy transfer and signal transceiving, multiple-input multiple-output (MIMO) communication in these networks is facing reliability/security performance degradation due to nonlinear EH, hardware impairment (HWi), and wire-tapping. The paper aims to numerically evaluate the security and reliability of MIMO communication in EH-assisted NOMA CR networks with jamming (MehNOwJ) under such effects. The results indicate that MehNOwJ prevents full outage and achieves optimum performance with proper parameter selection of preset spectral efficiency, power saturation threshold, EH duration, number of antennas of jammer. In addition, the performance improves with an accreting quantity of antennas but experiences saturation. Moreover, MehNOwJ drastically outperforms alternative approaches (MIMO communication in EH-assisted orthogonal multiple access CR network with jamming and MIMO communication in EH-assisted NOMA CR network without jamming), offering insights into the benefits of combining NOMA and jamming techniques.

The frequency hopping pattern of the existing frequency hopping communication system is not designed according to the electromagnetic interference environment, resulting in blind anti-jamming. Therefore, to address this problem, a “three-variable” frequency-hopping pattern is proposed, where the frequency, hopping rate, and instantaneous bandwidth of the frequency-hopping signal vary randomly based on the background electromagnetic interference. The decision-making problem of the “three-variable” frequency-hopping pattern is modeled as a Markov decision process (MDP) by constructing the state-action-reward tuple. The designed frequency varies continuously within a small frequency band selected from a pseudo-random sequence to alleviate the problem of dimension explosion in decision-making. At the same time, discrete values for the hopping rate and instantaneous bandwidth are designed. To solve this MDP problem efficiently, a combined deep reinforcement learning algorithm (OC-CDRL) based on optimistic exploration and conservative estimation is proposed, which combines the features of TD3 and D3QN algorithms and designs the corresponding states, actions, and rewards to deal with continuous and discrete action spaces, respectively. To address the problem that the D3QN algorithm tends to fall into local optimal solutions, an optimistic exploration strategy (OES) for action selection is proposed to improve the degree of exploration. Moreover, the loss function is improved by conservatively estimating state–action pairs outside the experience replay buffer, reducing the overestimation of the optimistic action-value function and increasing the stability and convergence of the algorithm. Comparative simulation results of the algorithms in different electromagnetic interference environments show that the OC-CDRL algorithm effectively avoids most regions with higher interference and has better adaptability and anti-jamming capability.

Cognitive radio (CR) is of crucial importance in providing efficient management of limited spectrum resources. However, its performance relies on efficient spectrum sensing. This paper investigates a novel approach for CR networks that leverages intelligent reflecting surface (IRS) specifically for spectrum sensing and non-orthogonal multiple access (NOMA) for data transmission. We propose a Grey-Wolf Optimization (GWO) based IRS optimization approach to maximize spectrum sensing performance. Independent of the IRS, NOMA is employed to improve spectral efficiency during data transmission. The performance is evaluated in terms of throughput and spectrum sensing parameters, namely probability of false alarm and missed detection. Numerical and simulation results demonstrate that GWO-based IRS optimization significantly outperforms conventional nature-inspired algorithms, achieving approximately 97% improvement in spectrum sensing accuracy. Based on the improved spectrum sensing results, the effective data transmission throughput is evaluated and validated through extensive simulation.

This work studies the throughput fairness among multiple Internet of Things (IoT) nodes in a backscatter assisted cognitive radio network, where the primary transmitter conveys its long-packet information to its receiver while multiple IoT nodes alternate backscattering their short-packet information to the information receiver via backscatter communication (BackCom). Specifically, we devise a non-convex problem aimed at ensuring the throughput fairness among IoT nodes concerning their transmitted data bits, by jointly optimizing the short-packet blocklength, packet error rate, and power reflection coefficient of each IoT node. Employing the block-coordinated-decent (BCD), the original problem is decoupled into two subproblems, both of which are solved by the proposed golden section based iterative algorithm and the proposed successive convex approximation (SCA) based iterative algorithm, respectively. Then a BCD based iterative algorithm is developed to solve the original problem. Simulations demonstrate the rapid convergence and superiority of the proposed algorithm over several baseline schemes in achieving the fairness of transmission bits.

Mobile Cloud-Edge Collaboration (MCEC) views in the main of converting the site for user electronics. By naturally integrating mobile devices with cloud computing (CC) resources at the edge of the scheme, this mutual paradigm improves storage, processing, and communication capabilities. This cooperation increases the performance of user electronics, delivering users responsive and resource-efficient knowledge. Offloading in Mobile Cloud-Edge Collaboration (MCEC) is a strategic device that recovers computational efficiency and resource energy for mobile devices. By reasonably moving computation tasks from mobile devices to the edge or cloud servers, offloading declines the load on the limited processing and energy capabilities of mobile devices. This joint method influences the stable computing power and storage aptitude accessible in the cloud-edge structure, confirming that resource-intensive uses like complex data processing or machine learning (ML) tasks can be implemented professionally. Offloading not only increases the receptiveness and performance of mobile users but also contributes to energy conservation, extending the battery time of mobile devices. This study proposes an African Vultures Optimizer algorithm-based Offloading Strategy for Mobile Cloud-Edge Collaboration (AVOAOS-MCEC) approach for consumer electronics. The AVOAOS-MCEC technique is based on the nature of AVOA is a new nature-based system, which is inspired by the unusual behavior of African vultures in foraging and navigation. In addition, the AVOAOS-MCEC technique designs a task offloading process to reduce the total energy utilization with the fulfillment of capacity and delay requirements. The experimental validation of the AVOAOS-MCEC method is verified utilizing distinct measures. An extensive comparison study stated that the AVOAOS-MCEC technique outperforms the other models in terms of several performance measures.

This paper proposes a reconfigurable intelligent surface (RIS)-enhanced unmanned aerial vehicle (UAV) secure communicating scheme with multiple mutually-untrusted ground users (GUs). To resist eavesdropping, a pre-defined artificial noise (AN) is added to the intended signal at UAV for scheduled GUs in each time slot. The GU scheduling schemes, UAV trajectories and transmit power allocations in different time slots are jointly designed to ensure communication security for all GUs. To boost secrecy rate of the system while ensuring secure fairness among GUs, we conduct a joint optimization process, involving optimizing UAV trajectories, RIS phase shifts, GU scheduling schemes, and UAV transmit power splitting factors, adhering to the constraints of UAV mobility, RIS phase shifts and other related constraints. To solve this non-convex optimization, we utilize the block coordinate descent (BCD) method for problem decomposition, coupled with an iterative algorithm to optimize the resulting sub-problems. To further solve the non-convex sub-problems, we employ the variable substitution to convexify the transmit power allocation, the semi-deterministic relaxation (SDR) to convexify the RIS passive beamforming and successive convex approximation (SCA) to convexify UAV mobility constraints. Simulation results show the convergence and effectiveness of the proposed scheme, compared to the benchmark schemes, the average worst-case secrecy rate increases by 32.33%, 60.38% and 80.09‘% respectively.

Cloud–edge–end collaborative computational task offloading (CEETO) is a promising method in industrial Internet-of-things (IIoT) to support massive computational tasks generated by equipment that has low energy/computation ability. In this work, we propose a new CEETO scheme by invoking the deep learning method (DL) with the aid of a multi-information analysis approach. Firstly, considering the delay constraints of the real-time tasks and the processing ability constraints of the cloud/edge/end servers, we formulate the CEETO problem to achieve the lowest system delay by establishing contact between CEETO problem and multiple information, such as the time-related locations and tasks requirements/features. Then, we tailor a long-short term memory network (LSTMN) to analyze the relation among time, locations and task requirements/features for predicting multiple information. Finally, the predicted multiple information is utilized for the final offloading strategy generation by invoking the simulated annealing algorithm (SAA). As the proposed CEETO process is invoked based on the predictions of multiple information, it is particularly suitable for the planning, scheduling and deployment of cloud–edge–end resources in massive equipment IIoT scenarios. Simulation results show that our proposed scheme can achieve effective computational task offloading.