Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
Atmospheric ducting has a significant impact on electromagnetic wave propagation. Radio signals that are trapped and guided by the atmospheric duct can travel a much longer distance over the horizon with lower attenuation since the signal power does not spread isotropically through the atmosphere.Atmospheric ducting brings both challenges and opportunities to wireless communications. On one hand, the signals propagating in the atmospheric duct may interfere with a receiver far away as remote co-channel interference. On the other hand, a point-to-point link can be established directly through the atmospheric duct to enable beyond line-of-sight communications. In this article, the formation of the atmospheric duct and its effects on radio wave propagation are first overviewed. Then solutions and standardization activities in the 3rd Generation Partnership Project (3GPP) to mitigate atmospheric duct induced remote interference are presented. Finally, the applications and design challenges of atmospheric duct enabled beyond line-of-sight communications are reviewed and future research directions are suggested.
This paper studies a federated edge learning system, in which an edge server coordinates a set of edge devices to train a shared machine learning (ML) model based on their locally distributed data samples. During the distributed training, we exploit the joint communication and computation design for improving the system energy efficiency, in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating ML-parameters are jointly optimized. In particular,we consider two transmission protocols for edge devices to upload ML-parameters to edge server, based on the non-orthogonal multiple access (NOMA) and time division multiple access (TDMA), respectively. Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy,by jointly optimizing the transmission power and rates at edge devices for uploading ML-parameters and their central processing unit (CPU) frequencies for local update. We propose efficient algorithms to solve the formulated energy minimization problems by using the techniques from convex optimization. Numerical results show that as compared to other benchmark schemes,our proposed joint communication and computation design significantly can improve the energy efficiency of the federated edge learning system,by properly balancing the energy tradeoff between communication and computation.
With the rapid growth of wireless data demand and the shortage of global bandwidth,the use of millimeter-wave (mmWave) frequency band for wireless cellular networks has become the core content of the fifth generation cellular network. Because mmWave communication has different characteristics from microwave communication,using traditional optimization techniques to manage the resource of mmWave communication networks is inappropriate. In this paper, we propose a neural network-based algorithm to solve the joint user association and resource allocation for mmWave communication system with multi-connectivity(MC)and integrated access backhaul(IAB).The resource allocation problem is formulated as a mixed-integer quadratically constrained quadratic programming(MIQCQP),which is very difficult to solve. First,we decompose the MIQCQP into two sub-problems, i.e., binary associated matrix sub-problem and continuous IAB ratio sub-problem. Then we propose a neural network to solve the binary associated matrix inference problem and a resource allocation algorithm to find the sub-optimal IAB ratio. Simulation results show that the proposed algorithm can achieve good performance with a fast inference speed.
Considering the global demands on Internet of things (IoT), and the limitation of constructing base stations for the terrestrial IoT, the satellite IoT approach is a realizable and powerful supplement to the terrestrial IoT. Meanwhile, in order to dynamically access the available terrestrial and satellite networks, IoT terminals may have the ability of accessing both the terrestrial IoT and the satellite IoT, leading to great challenges on the access-control of the IoT.In this paper, we design a satellite-terrestrial integrated architecture for the IoT relying on the software defined network (SDN). Moreover, based on this architecture, we further propose a dynamic channel resource allocation algorithm to control the access of the IoT terminals with different priorities. Simulation results show that the demands on the probabilities of successful access of IoT terminals with various priorities can be simultaneously met if the access of the IoT terminals are well controlled.
The scale expansion of the space information networks(SINs)makes the demands for tacking,teleme-try and command(TT＆amp;C)missions increase dramatically. An increasing number of missions and a sharp conflict of resources make it much more challenging to schedule missions reasonably. In order to ensure both the mission completion rate of the high concurrent emergency mis-sions and the performance of regular missions,a conflict degree scheduling algorithm based on transfer strategy (CDSA-TS) is proposed concurrently reconfiguring multi-dimensional resources reasonably.Furthermore,we design an emergency mission planning algorithm based on simulated annealing algorithm(EMPA-SA)to increase the probability of jumping out of the trap through the iterative neighborhood searching strategy and destabiliza-tion. Finally,we design a simulation system to verify the network performance in terms of the integrated weights of completed missions and the time consumption of the proposed algorithms. We also investigate the impact of the scheduling strategy for emergency missions on regular missions to improve the overall network performance, which provides guidance for emergency mission planning in the future for the large scale constellation oriented SINs.
Dynamic adaptive streaming over HTTP (DASH) can adaptively select the appropriate video bitrate for mobile users. Mobile edge computing(MEC) scenario is of great benefit to improve the performance of mobile networks by providing computing and storage capabilities. And the utilization of spectrum resources can be improved by multicast transmission, but the performance of the multicast transmission will be directly affected by the selected grouping algorithm and resource allocation algorithm. In order to improve the quality of experience (QoE) of video users in the 5G MEC scenario,this paper proposes a QoE-driven DASH multicast scheme,which mainly covers the grouping algorithm and the adaptive bitrate (ABR) algorithm. First of all, we take the optimized target QoE as the basis for grouping and propose an adaptive grouping algorithm that can dynamically adjust the grouping results. Besides, we design a joint optimization ABR algorithm based on the prediction of QoE, which comprehensively considers the process of resource allocation and bitrate decision-making based on the prediction of QoE of video segments in a certain forward-looking field of view. The simulation results show that the proposed DASH multicast scheme performs well in QoE and fairness.
Legitimate surveillance has attracted more and more concern, and effective proactive intervention can eavesdrop the illegitimate information. In this paper, we propose legitimate eavesdropping over a two-hop suspicious communication link by two full-duplex legitimate monitors(LMs)based on multi-agent deep deterministic policy gradient (MADDPG) algorithm in two phases. In phase 1, the suspicious transmitter sends information to the suspicious assistant relay, and the assistant relay decodes and forwards the received message to the suspicious receiver in phase 2. Meanwhile, two LMs cooperatively emit jamming to suspicious relay and receiver during each phase. Particularly, each LM is considered to be an energy-limited device, and eavesdropping is a long-term process, so we adopt expected eavesdropping energy efficiency(EEE)over a period of time to evaluate eavesdropping performance. However, for two LMs, how to cooperatively make jamming power decision at each hop in a dynamic environment is a huge challenge. Therefore, MADDPG algorithm, as a multi-agent reinforcement learning approach with the advantage of dynamic decision-making, is utilized to solve the issue of jamming power decision for each LM.In the simulation, the results show that our proposed cooperative jamming scheme can obtain higher expected EEE.
Hybrid coordination function controlled channel access (HCCA) applied in wireless ad hoc network systems is the strategy to allocate the bandwidth resources of control channel(CCH)and service channels (SCHs). With the continuous development of Internet of vehicles(IoV)technologies, it is difficult for HCCA to support traffic information services, such as low delay along with high-frequency dissemination of security beacons and large capacity interaction of image data. Fusing the fundamental theories of No.7 signaling system and 5G-IoV technologies, this paper proposes a novel channel bandwidth allocation strategy named CO-HCCA, which matches CCH time slot numbers with on board units(OBUs)numbers to reduce the congestion of channel access demand information (CADI); and also illustrates a dynamic segment adjusting algorithm of the time slots. On one hand, in the channel reservation stage of CCH control cycle, OBUs are sorted according to the priority of data transmission,and then the corresponding transmission slots are adopted orderly according to the OBUs numbers,so as to decrease the collision probability of high-frequency dissemination of CADI. On the other hand, for the quickly arriving and leaving OBUs in coverage of roadside base station(RBS),the prediction of bandwidth segments is dynamically adjusted to adapt to the time-varying characteristic of the connected vehicle scenarios.Modeling calculation and objective comparison on OMNeT++computing platform show that the proposed CO-HCCA strategy can effectively reduce the channel congestion of IoV,and in the scenario of high-density data interaction, it is beneficial to promote the transmission timeliness of security beacons and the package delivery rate(PDR)of high bit-rate multiple information.
Vertical array codes have less computational complexity and update complexity in comparison with horizontal array codes. However, the fault tolerance of the existing vertical array codes is in general lower than that of horizontal array codes.In addition,the cross-rack bandwidth is often the bottleneck of the update performance in erasure-coded storage systems. In this paper, we propose a cross-rack update (CRU) mechanism for vertical array codes intended to improve both the fault tolerance and update performance of erasure-coded storage systems. CRU builds on three parts: (i)stripe encoding, which can improve the fault tolerance of vertical code by encoding multiple sub-stripe; (ii) node grouping, which filters out the best combination of nodes to minimize cross-rack update traffic;(iii)selective logging,which can selectively log based on the location of data sub-blocks and parity sub-blocks to reduce disk I/O and cross-rack traffic. We evaluate CRU via trace-driven analysis and local cluster experiments.Evaluations show that CRU can significantly reduce cross-rack update traffic and improve system update throughput.
