Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
In this paper,we design a resource management scheme to support stateful applications, which will be prevalent in sixth generation(6G)networks. Different from stateless applications, stateful applications require context data while executing computing tasks from user terminals(UTs). Using a multi-tier computing paradigm with servers deployed at the core network, gateways, and base stations to support stateful applications, we aim to optimize long-term resource reservation by jointly minimizing the usage of computing,storage,and communication resources and the cost of reconfiguring resource reservation. The coupling among different resources and the impact of UT mobility create challenges in resource management. To address the challenges, we develop digital twin(DT)empowered network planning with two elements, i.e., multi-resource reservation and resource reservation reconfiguration. First, DTs are designed for collecting UT status data, based on which UTs are grouped according to their mobility patterns. Second,an algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands. Last, a Meta-learning-based approach is developed to reconfigure resource reservation for balancing the network resource usage and the reconfiguration cost. Simulation results demonstrate that the proposed DTempowered network planning outperforms benchmark frameworks by using less resources and incurring lower reconfiguration costs.
In recent years, reinforcement learning (RL) has shown high potential for robotic applications. However, RL heavily relies on the reward function, and the agent merely follows the policy to maximize rewards but lacks reasoning ability. As a result, RL may not be suitable for long-horizon robotic tasks. In this paper, we propose a novel learning framework, called multiple state spaces reasoning reinforcement learning(SRRL),to endow the agent with the primary reasoning capability. First, we abstract the implicit and latent links between multiple state spaces. Then, we embed historical observations through a long short-term memory (LSTM) network to preserve long-term memories and dependencies. The proposed SRRL’s ability of abstraction and long-term memory enables agents to execute long-horizon robotic searching and planning tasks more quickly and reasonably by exploiting the correlation between radio frequency identification (RFID) sensing properties and the environment occupation map. We experimentally validate the efficacy of SRRL in a visual game-based simulation environment. Our methodology outperforms three state-of-the-art baseline schemes by significant margins.
This paper investigates the reliability problem of airborne free-space optical(FSO)communications, and a hybrid FSO/radio frequency (RF) communication system with parallel transmission is proposed, where the data stream is transmitted over both FSO and RF links simultaneously. Further,to combat channel fading, maximal ratio combining is utilized at the receiver for combining received signals from both links. The performances of the proposed system are analytically derived in terms of the outage probability and the average bite-error rate (BER). Numerical results show that the proposed hybrid FSO/RF system with parallel transmission outperforms a single airborne FSO or a single RF link, which provides technical guidance for designing reliable high-speed airborne communication systems.
In this paper, we propose a transmission scheme for uplink and downlink transmissions,where the fifth generation (5G) low-density parity-check (LDPC) codes are implemented for error correction. In the proposed scheme,the acknowledgment(ACK)or negative acknowledgment(NACK)feedback information is transmitted along with the payload data by cyclically shifting coded sequence, while the re-transmitted codewords are superimposed (XORed) partially on the current codewords. The distinguished feature of the proposed transmission scheme is that it requires neither extra transmission bandwidth nor extra transmission power. We also propose to truncate the error patterns for the purpose of reducing the implementation complexity and reducing the error propagation. Numerical results show that the proposed scheme significantly outperforms conventional LDPC-coded transmission. For the 5G LDPC code with length 1 920 at the signal-to-noise ratio(SNR)of 1.3 dB,the word error rate(WER)of the data transmitted by the proposed scheme is about 10-4, while that of the conventional LDPC-coded transmission is about 10-2.
Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication (ISAC), which highly depends on the accuracy of the channel prediction (CP), i.e., predicting the angular parameters of users. However, the performance of CP highly depends on the estimated historical channel stated information (CSI) with estimation errors, resulting in the performance degradation for most traditional CP methods. To further improve the prediction accuracy, in this paper, we focus on the ISAC in vehicle networks and propose a convolutional long-short term memory (CLSTM) recurrent neural network (CLRNet) to predict the angle of vehicles for the design of predictive beamforming. In the developed CLRNet, both the convolutional neural network (CNN) module and the LSTM module are adopted to exploit the spatial features and the temporal dependency from the estimated historical angles of vehicles to facilitate the angle prediction. Finally, numerical results demonstrate that the developed CLRNet-based method is robust to the estimation error and can significantly outperform the state-of-the-art benchmarks, achieving an excellent sum-rate performance for ISAC systems.
Fifth generation (5G) cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements, which are not achieved by single network technology. The future cellular networks require efficient resource allocation and power control schemes that meet throughput and energy efficiency requirements when multiple technologies coexist and share network resources. In this paper, we optimize the throughput and energy efficiency (EE) performance for the coexistence of two technologies that have been identified for the future cellular networks, namely, massive multiple-input multiple-output (MIMO) and network-assisted device-to-device(D2D)communications. In such a hybrid network, the co/cross-tier interferences between cellular and D2D communications caused by spectrum sharing is a significant challenge. To this end, we formulate the average sum rate and EE optimization problem as mixed-integer non-linear programming (MINLP). We develop distributed resource allocation algorithms based on matching theory to alleviate interferences and optimize network performance. It is shown in this paper that the proposed algorithms converge to a stable matching and terminate after finite iterations.Matlab simulation results show that the proposed algorithms achieved more than 88% of the average transmission rate and 86% of the energy efficiency performance of the optimal matching with lower complexity.
Array imperfections will lead to serious performance degradation of the deep neural network(DNN) based direction of arrival (DOA) estimation in the low earth orbit (LEO) satellite communication by producing a mismatch between inference data and training data. In this paper,we propose a lightweight deep learning-based algorithm for array imperfection correction and DOA estimation. By preprocessing the covariance matrix of the array antenna outputs to the image, the array imperfection correction and DOA estimation problems are correspondingly converted into the image-to-image transformation task and image recognition task. Furthermore, for the deployment of real-time DNN-based DOA estimation on the resource-constrained edge system, generative adversarial network(GAN)model compression is applied to obtain a lightweight student generator of Pix2Pix for array imperfection correction. The Mobilenet-V2 is then used to extract the DOA information from the covariance matrix image. Simulations results demonstrate that the DOA estimation performance is significantly improved through the array imperfection correction. The proposed algorithm also better satisfies the real-time demand with decreased inference time on the resource-constrained edge system.
The present paper proposes a secure design of the energy-efficient multi-modular exponential techniques that use store and reward method and store and forward method. Computation of the multi-modular exponentiation can be performed by three novel algorithms:store and reward, store and forward 1-bit (SFW1), and store and forward 2-bit (SFW2). Hardware realizations of the proposed algorithms are analyzed in terms of throughput and energy. The experimental results show the proposed algorithms SFW1 and SFW2 increase the throughput by orders of 3.98% and 4.82%, reducing the power by 5.32% and 6.15% and saving the energy in the order of 3.95% and 4.75%, respectively. The proposed techniques can prevent possible side-channel attacks and timing attacks as a consequence of an inbuilt confusion mechanism. Xilinx Vivado-21 on Virtex-7 evaluation board and integrated computer application for recognizing user services (ICARUS) Verilog simulation and synthesis tools are used for field programmable gate array (FPGA) for hardware realization. The hardware compatibility of proposed algorithms has also been checked using Cadence for application specific integrated circuit(ASIC).
For the problem of multiplexing multimodal vortex electromagnetic waves, a double-ring concentric uniform circular array(CUCA)consisting of 12 circularly polarized antennas (4 inner rings and 8 outer rings) is proposed in this paper. A complex feeding network is solved by rotating the circularly polarized antennas at a certain angle. The antennas are rotationally symmetric and point to the center, generating orbital angular momentum (OAM) waves by feeding the same amplitude and phase signals. In addition, this paper combines millimeter wave (mm-wave) and ultra-wideband (UWB) with OAM. The proposed antenna array can generate OAM beams at 30～40 GHz with l=-1,-2.When l=-1 the relative bandwidth is 25.2% and the gain is 8.03 dBi;when l=-2 the relative bandwidth is 27.7% and the gain is 9.43 dBi. The analysis of simulation results shows that the antenna array has UWB performance,good gain,and a standard spiral phase distribution, which can provide some practical significance for modal multiplexing of mm-wave band OAM.
Using unmanned aerial vehicles (UAVs) to collect data in wireless sensor networks (WSNs) has advantages of controllable mobility and flexible deployment. However, there are potential challenges of energy limitation and data security which may limit such applications. To cope with these challenges, a complicated and intractable optimization problem is formulated, which maximizes the performance metric of secrecy energy efficiency (EE) subject to the constraints of secrecy rate, maximum power, and trajectory. Then, an energy-efficient and secure solution is developed to improve the secrecy EE of the UAV-enabled data collection in the WSNs by joint optimizing the UAV’s trajectory and velocity along with the sensors’power. The proposed solution is an iterative algorithm based on the optimization approaches of alternating optimization,successive convex approximation,and fractional programming. Simulation results demonstrate that the proposed solution scheme is effective for improving the secrecy EE while guaranteeing the data security.
