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In recent years, semantic communication based on deep joint source-channel coding (DEEPJSCC) has been demonstrated and widely investigated. However, existing DEEPJSCC schemes suffer from low efficiency in mining latent semantic representations, as well as large model size, high computational complexity, and redundant parameters. To address these issues, we meticulously establish a lightweight DEEPJSCC framework for wireless image semantic transmission, termed STARJSCC. The proposed method achieves flexible wireless image transmission by introducing an improved channel state adaptive module (CSA Mod) to adapt to different channel conditions, combined with a decoupled static semantic compression (SC) mask to control different transmission rates. Experimental results show that the STARJSCC framework outperforms other baseline schemes in terms of performance and adaptability across various transmission rates and signal-to-noise ratio (SNR) levels, achieving up to 2.73 dB improvement on high-resolution test set. Moreover, this solution significantly reduces model parameters, computational complexity, and storage overhead, providing a potential solution for high-quality wireless image transmission in resource-constrained scenarios.

Efficient utilization of network resources, particularly channel bandwidth allocation, is critical for optimizing the overall system performance and ensuring fair resource allocation among multiple distributed computing nodes. Traditional methods for channel bandwidth allocation, based on fixed allocation schemes or static heuristics, often need more adaptability to dynamic changes in the network and may not fully exploit the system’s potential. To address these limitations, we employ reinforcement learning algorithms to learn optimal channel allocation policies by intermingling with the environment and getting feedback on the outcomes of their actions. This allows devices to adapt to changing network conditions and optimize resource usage. Our proposed framework is experimentally evaluated through simulation experiments. The results demonstrate that the framework consistently achieves higher system throughput than conventional static allocation methods and state-of-the-art bandwidth allocation techniques. It also exhibits lower latency values, indicating faster data transmission and reduced communication delays. Additionally, the hybrid approach shows improved resource utilization efficiency, efficiently leveraging the strengths of both Q-learning and reinforcement learning for optimized resource allocation and management.

To provide high-quality streaming services in device-to-device (D2D) communications, performance parameters such as encoding rate, decoding rate, and flow rate should be detected and monitored. The proposed algorithm provides a method to detect time streaming for traffic flows in D2D communications, and a sequence to detect rate imbalance. This paper proposes a new FS-CDA (flow sensing-based congestion detecting algorithm) to prevent high congestion rates and assist an optimized D2D streaming service in 5G-based wireless mobile networks. The proposed algorithm detects and controls flow imbalance for streaming segments during D2D communications, and it includes operations such as transmission rate monitoring, rate adjustment functions, and underflow and overflow sensing for these operations. The paper aims to effectively control traffic flow rates caused by adjacent channel bandwidth, high bit rate error, and heterogeneous radio interference, and to enhance the performance of D2D streaming services by performing such operations. The proposed algorithm for D2D streaming services is measured by deriving the individual weight of certain versions of a streaming flow. Based on the given operations, the simulation results indicated that the proposed algorithm has better performance with respect to average congestion control ratio, PSNR, and average throughput than other methods.

The stabilizability problem of an unstable non‐minimum phase (NMP) plant, controlled over a signal‐to‐noise ratio (SNR) constrained channel with transmission delay, is investigated in this article. A dynamical output feedback controller is used to stabilize the single‐input single‐output plant where the control data are exchanged via an NMP bandwidth‐limited communication medium. For the first time, a novel description of SNR is used to solve stabilizability problem when the NMP channel imposes a constant delay on transmitted data. It is demonstrated that the presence of time‐delay in the channel model as well as its NMP zeros increases the value of SNR needed for stabilizability of the closed‐loop system. The main results are illustrated and discussed through numerical examples. © 2016 Wiley Periodicals, Inc. Complexity 21: 557–565, 2016

The 5G frequency band is extremely close to the operating frequency band of radio altimeters (RAs), so an in-depth study of the possible interference of 5G’s key parameters on RAs is especially necessary to ensure aviation safety. In this paper, the interference magnitude of 5G waveforms on an altimeter was measured by simulating the Adjacent Channel Leakage Power Ratio (ACLR) values for different sub-carrier spacing (SCS) and channel bandwidth configurations. Furthermore, interference injection experiments on simulated 5G signals and the interference thresholds of a frequency-modulated continuous wave (FMCW) altimeter were compared to experiments on the effects of the different configurations of 5G SCSs, channel bandwidths, and center frequency points. The interference thresholds of this FMCW altimeter were found to be in the range of 1 dBm to 6 dBm and −4 dBm to 0 dBm under the interference of 5G signals at the center frequency points of 3.7 GHz and 3.9 GHz. These results provided a certain reference for the engineering judgement margin of the interference thresholds.

This paper investigates the feasibility of using the three-dimensional omnidirectional inductive channel for power transfer and as a power line communication (PLC) for ground-based vehicle, electric air vehicle, or space applications. The simulation results were performed by the advanced design system software using lumped equivalent circuit model. The power transfer efficiency was determined based on multiport scattering (S)-parameters numerical simulation results while the theoretical channel capacity was calculated based on Matlab software as a function of the coupling coefficient considering an additive white Gaussian noise. Furthermore, the magnetic field distribution was evaluated as function of the misalignment angle θ between the receiver and the three orthogonal transmitters coils.

Wireless local area network (WLAN) is the core of the classic wireless communications systems and owns the infrastructure which wide spreads in many regions in the world. IEEE 802.11n is an attractive standard of WLAN and offers a data capacity of the cell. This paper estimates the maximum limits of the IEEE 802.11n standard cell as a term of number of users which are successfully served by the cell in case of video conference application. The results shown that, the cell of 802.11n could serve about 9 users under the service of video conference in case of 20MHz channel bandwidth before congestion occurs while the 40MHz channel could support 18 users.

The rapid increase in the number of mobile phones and IoT devices connected to the network reduces the bandwidth of the Internet communication channel, and as a result, delays occur in the delivery of data processed in remote clouds. Edge computing systems (cloudlet, fog computing, etc.) are used to eliminate resource shortages, energy consumption, and communication channel delays in mobile devices. Edge computing systems place processing devices (computers) close to users. Cloudlet-based mobile cloud computing is widely used to reduce delays in communication channels and energy consumption in mobile devices. Selection of the most suitable cloudlet allowing users to run applications fast in cloud is still a considerable problem. This paper proposes a strategy for the selection of high-performance cloudlets providing fast solutions, considering the complexity of application (file type). It offers a method for cloudlet selection out of large number of cloudlets with different technical capabilities providing faster processing of user application. The timing of user applications in cloudlets with different technical capabilities (operating frequency, number of cores, volume of RAM, etc.) also varies. The proposed method provides faster solution for the user application. User applications are grouped by type of application, and a set of cloudlets are clustered by the number of groups. Clustering is performed first by the parameters corresponding to the operating frequency of the cloudlets, then by the number of cores and the volume of RAM. The proposed method reduces energy consumption of mobile devices by providing faster processing of applications. Thus, the proposed strategy provides an energy consumption reduction on mobile devices, faster processing of results and decrease of network delays.

This paper investigates the joint task allocation and resource optimization problem in an integrated radar and communication multi-UAV (IRCU) system. Specifically, we assign reconnaissance UAVs and communication UAVs to perform the detection, tracking and communication tasks under the resource, priority and timing constraints by optimizing task allocation, power as well as channel bandwidth. Due to complex coupling among task allocation and resource optimization, the considered problem is proved to be non-convex. To solve the considered problem, we present a loop iterative optimization (LIO) algorithm to obtain the optimal solution. In fact, the mentioned problem is decomposed into three sub-problems, such as task allocation, power optimization and channel bandwidth optimization. At the same time, these three problems are solved by the divide-and-conquer algorithm, the successive convex approximation (SCA) algorithm and the improved particle swarm optimization (PSO) algorithm, respectively. Finally, numerical simulations demonstrate that the proposed LIO algorithm consumes fewer iterations or achieves higher maximum joint performance than other baseline schemes for solving the considered problem.