Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications,sensing and computing

Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources.However,the overwhelming upload traffic may lead to unacceptable uploading time.To tackle this issue,for tasks taking environmental data as input,the data perceived by roadside units(RSU)equipped with several sensors can be directly exploited for computation,resulting in a novel task offloading paradigm with integrated communications,sensing and computing(I-CSC).With this paradigm,vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading.By optimizing the computation mode and network resources,in this paper,we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task.Although this nonconvex problem can be handled by the alternating minimization(AM)algorithm that alternatively minimizes the divided four sub-problems,it leads to high computational complexity and local optimal solution.To tackle this challenge,we propose a creative structural knowledge-driven meta-learning(SKDML)method,involving both the model-based AM algorithm and neural networks.Specifically,borrowing the iterative structure of the AM algorithm,also referred to as structural knowledge,the proposed SKDML adopts long short-term memory(LSTM)networkbased meta-learning to learn an adaptive optimizer for updating variables in each sub-problem,instead of the handcrafted counterpart in the AM algorithm.Furthermore,to pull out the solution from the local optimum,our proposed SKDML updates parameters in LSTM with the global loss function.Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.

V2X Offloading and Resource Allocation in SDN-Assisted MEC-Based Vehicular Networks

As an important application scenario of 5G, the vehicular network has a huge amount of computing data, which brings challenges to the scarce network resources. Mobile edge computing(MEC) sinks cloud services to the edge of network, which reduces the delay jitter caused by remote cloud computing. Software-defined networking(SDN) is an emerging network paradigm with the features of logic centralized control and programmability. In this paper, we construct an SDN-assisted MEC network architecture for the vehicular network. By introducing SDN controller, the efficiency and flexibility of vehicular network are improved, and the network state can be perceived from the global perspective. To further reduce the system overhead, the problem of vehicle to everything(V2X) offloading and resource allocation is proposed, where the optimal offloading decision, transmission power control, subchannels assignment, and computing resource allocation scheme are given. The optimization problem is transformed into three stages because of the heterogeneity of the offloaded tasks and the NP-hard property of the problem. Firstly, the analytic hierarchy process is used to select initial offloading node, then stateless Q-learning is adopted to allocate transmission power, subchannels and computing resources. In addition, the offloading decision is modeled as a potential game, and the Nash equilibrium is proved by the potential function construction. Finally, the numerical results show that the proposed mechanism can effectively reduce the system overhead and achieve better results compared with others’ algorithms.

Dynamic Optimization of Handover Parameters Adjustment for Conflict Avoidance in Long Term Evolution

In order to achieve dynamical optimization of mobility load balancing,we analyze the conflict between mobility load balancing and mobility robustness optimization caused by the improper operation of handover parameters.To this end,a method of Handover Parameters Adjustment for Conflict Avoidance(HPACA)is proposed.Considering the movement of users,HPCAC can dynamically adjust handover range to optimize the mobility load balancing.The movement of users is an important factor of handover,which has a dramatic impact on system performance.The numerical evaluation results show the proposed approach outperforms the existing method in terms of throughput,call blocking ratio,load balancing index,radio link failure ratio,ping-pong handover ratio and call dropping ratio.

Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learning

In this paper,to deal with the heterogeneity in federated learning(FL)systems,a knowledge distillation(KD)driven training framework for FL is proposed,where each user can select its neural network model on demand and distill knowledge from a big teacher model using its own private dataset.To overcome the challenge of train the big teacher model in resource limited user devices,the digital twin(DT)is exploit in the way that the teacher model can be trained at DT located in the server with enough computing resources.Then,during model distillation,each user can update the parameters of its model at either the physical entity or the digital agent.The joint problem of model selection and training offloading and resource allocation for users is formulated as a mixed integer programming(MIP)problem.To solve the problem,Q-learning and optimization are jointly used,where Q-learning selects models for users and determines whether to train locally or on the server,and optimization is used to allocate resources for users based on the output of Q-learning.Simulation results show the proposed DT-assisted KD framework and joint optimization method can significantly improve the average accuracy of users while reducing the total delay.

Erratum to “A robust beam tracking scheme for millimeter wave HetNets” [Digit. Commun. Network. 8 (6) (2022) 1115–1121]

The Editorial office regrets that a note about the affiliation of the first author Qing Xue was omitted in the initially published version of this paper.The note is that Qing Xue was co-first affiliated with the UESTC and CQUPT for the work of this paper.

Energy-Efficient Power Allocation for IoT Devices in CR-NOMA Networks

Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks.To improve the energy efficiency(EE)of the unlicensed secondary users(SU),a power allocation(PA)algorithm with polynomial complexity is investigated.We first establish the feasible range of power consumption ratio using Karush-Kuhn-Tucker optimality conditions to support each SU’s minimum quality of service and the effectiveness of successive interference cancellation.Then,we formulate the EE optimization problem considering the total transmit power requirements which leads to a non-convex fractional programming problem.To efficiently solve the problem,we divide it into an inner-layer and outer-layer optimization sub-problems.The inner-layer optimization which is formulated to maximize the sub-carrier PA coefficients can be transformed into the difference of convex programming by using the first-order Taylor expansion.Based on the solution of the inner-layer optimization sub-problem,the concave-convex fractional programming problem of the outer-layer optimization sub-problem may be converted into the Lagrangian relaxation model employing the Dinkelbach algorithm.Simulation results demonstrate that the proposed algorithm has a faster convergence speed than the simulated annealing algorithm,while the average system EE loss is only less than 2%.

Identifying influential nodes in social networks via community structure and influence distribution difference

This paper aims to effectively solve the problem of the influence maximization in social networks.For this purpose,an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed.Firstly,the network embedding-based community detection approach is developed,by which the social network is divided into several high-quality communities.Secondly,the solution of influence maximization is composed of the candidate stage and the greedy stage.The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm,and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm.Finally,experimental results demonstrate the superiority of the proposed method compared with existing methods,from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.

A novel low-complexity power allocation algorithm based on the NOMA system in a low-speed environment

For future wireless communication systems,Power Domain Non-Orthogonal Multiple Access(PD-NOMA)using an advanced receiver has been considered as a promising radio access technology candidate.Power allocation plays an important role in the PD-NOMA system because it considerably affects the total throughput and Geometric Mean User Throughput(GMUT)performance.However,most existing studies have not completely accounted for the computational complexity of the power allocation process when the User Terminals(UTs)move in a slow fading channel environment.To resolve such problems,a power allocation method is proposed to considerably reduce the search space of a Full Search Power(FSP)allocation algorithm.The initial power reallocation coefficients will be set to start with former optimal values by the proposed Lemma before searching for optimal power reallocation coefficients based on total throughput performance.Step size and correction granularity will be adjusted within a much narrower power search range while invalid power combinations may be reasonably discarded during the search process.The simulation results show that the proposed power reallocation scheme can greatly reduce computational complexity while the total throughput and GMUT performance loss are not greater than 1.5%compared with the FSP algorithm.

Joint Channel Estimation and Decoding Design for 5G-Enabled V2V Channel

This paper addresses the problem of channel estimation in 5G-enabled vehicular-to-vehicular(V2V) channels with high-mobility environments and non-stationary feature. Considering orthogonal frequency division multiplexing(OFDM) system, we perform extended Kalman filter(EKF) for channel estimation in conjunction with Iterative Detector & Decoder(IDD) at the receiver to improve the estimation accuracy. The EKF is proposed for jointly estimating the channel frequency response and the time-varying time correlation coefficients. And the IDD structure is adopted to reduce the estimation errors in EKF. The simulation results show that, compared with traditional methods, the proposed method effectively promotes the system performance.

Cognitive MAC Protocol with Minimum Sampling Time and Cross-Layer Cooperation for Low Signal-to-Noise Ratio Environments

The previous Decentralised Cognitive Medium Access Control(DC-MAC) protocol allows Secondary Users(SUs) to independently search for spectrum access opportunities without the need for a central coordinator.DC-MAC assumes that the detection scheme is ideal at the Physical(PHY) layer.In fact,a more complex detection algorithm is impractical in distributed spectrum sharing scenarios.Energy Detection(ED) at the PHY layer has become the most common method because of its low computational and implementation complexities.Thus,it is essential to integrate the DC-MAC with ED at the PHY layer.However,ED requires the Minimum Sampling Time(MST)duration to achieve the target detection probability in low Signal-to-Noise Ratio(SNR)environments.Otherwise,it cannot achieve the expected detection performance.In this paper,we derive an accurate expression of MST for ED in low SNR environments.Then,we propose an Optimised DC-MAC(ODC-MAC) protocol which is based on MST,and which amends the aforementioned problems of DC-MAC with ED.Moreover,the closed-form expressions for the unreliable data transmission probability are derived for both DC-MAC and ODC-MAC.We show that the simulation results agree well with the theoretical analyses.The proposed ODC-MAC can improve the data transmission reliability and enhance the throughput compared to the performance of the traditional DC-MAC.

A robust beam tracking scheme for millimeter wave HetNets

Millimeter Wave(mmWave)communication has been widely acknowledged as an attractive solution to address high-speed transmission of massive data in 5G and beyond 5G systems due to the promising spectrum availability.However,mmWave signals are highly susceptible to blockage and may suffer from rapidly changing channels.Thus,directional/beam tracking becomes imperative yet essential for robust mmWave communications.To address this challenge,we propose a robust beam tracking scheme for mmWave Heterogeneous Networks(HetNets)with multi-connectivity.Different from most existing schemes,the proposed beam tracking scheme is effective for outage events.We first discuss theμWave-assisted beam tracking procedure with and without candidate beams,and then analyze the inherent correlation between mmWave link quality and the operating beamwidth and occlusion range to derive the optimal beamwidth.Theoretical and numerical results show that the proposed beam tracking scheme can improve the robustness of mmWave communications while guaranteeing the rate performance.

SMDP-based sleep policy for base stations in heterogeneous cellular networks

A dense heterogeneous cellular network can effectively increase the system capacity and enhance the network coverage.It is a key technology for the new generation of the mobile communication system.The dense deployment of small base stations not only improves the quality of network service,but also brings about a significant increase in network energy consumption.This paper mainly studies the energy efficiency optimization of the Macro-Femto heterogeneous cellular network.Considering the dynamic random changes of the access users in the network,the sleep process of the Femto Base Stations(FBSs)is modeled as a Semi-Markov Decision Process(SMDP)model in order to save the network energy consumption.And further,this paper gives the dynamic sleep algorithm of the FBS based on the value iteration.The simulation results show that the proposed SMDP-based adaptive sleep strategy of the FBS can effectively reduce the network energy consumption.

Joint use of time-domain and windowed method for design of modulated DFT filter banks

A novel and efficient technique to design modulated discrete Fourier transform （DFT） filter banks is introduced in this paper. The proposed method first relaxes the limits that the synthesis filters are the time-reversed version of the analysis filters and then adopts the time domain formula of the perfect reconstruction property as the solution to design the synthesis filters. The prototype filter in analysis filter banks is designed based on Fourier-Kaiser window approach. Simulation results show that the designed filter banks approximately satisfy the perfect reconstruction with controllable reconstruction errors.

Secrecy performance analysis of single-input multiple-output generalized-K fading channels

In this paper, the transmission of confidential messages through single-input multiple-output （SIMO） independent and identically generMized-K （KG） fading channels is considered, where the eavesdropper overhears the transmission from the transmitter to the receiver. Both the receiver and the eavesdropper are equipped with multiple antennas, and both active and passive eavesdroppings are considered where the channel state information of the eavesdropper＇s channel is or is not available at the transmitter. The secrecy performance of SIMO KG systems is investigated. Analytical expressions for secrecy outage probability and average secrecy capacity of SIMO systems are derived via two different methods, in which KG distribution is approximated by the Gamma and mixture Gamma distributions, respectively. Numerical results are presented and verified via the Monte-Carlo simulation.

Performance analysis of complexity reduction BP decoding of rateless codes by deleting low reliable symbols

This paper investigates the performance of the method used to reduce the decoding complexity of rateless codes through the deletion of the received symbols with low reliability. In the decoder, the received symbols whose absolute value of logarithm likelihood ratio （LLR） is lower than the threshold are removed, together with their corresponding edges, and thus not involved in the decoding process. The relationship between the deletion probability and the likelihood ratio deletion threshold is derived. The average mutual information per received symbol is analyzed in the case of deletion. The required number of symbols for the decoder to keep the same performance as regular decoding decreases since the average mutual information per symbol increases with the deletion, thus reducing the decoding complexity. This paper analyzes the reduction of decoding computations and the consequent transmission efficiency loss from the perspective of mutual information. The simulation results of decoding performance are consistent with those of the theoretical analysis, which show that the method can effectively reduce the decoding complexity at the cost of a slight loss of transmission efficiency.

Clustering routing algorithm of self-energized wireless sensor networks based on solar energy harvesting

Aiming at the problems of existing clustering routing algorithm of self-energized wireless sensor networks（WSNs） on fixed threshold for resurrection, incapacitates reappoint cluster head in the next round and lack of election limit, this paper proposes a novel clustering routing algorithm for self-energized WSNs clustering routing algorithm based on solar energy harvesting（CRBS） algorithm. The algorithm puts forward a threshold sensitive resurrection mechanism, reviving the node when harvesting energy reaches the set soft or hard energy threshold. Meanwhile, combined with current energy harvesting level, cluster head node can decide whether to reappoint the cluster head in the next round. What＇s more, CRBS optimizes the cluster head election threshold to limit the incompetent node in election. Combined with the solar energy harvesting simulation, the results show that CRBS algorithm can better keep the default cluster head proportion, and outperforms energy balanced clustering with self-energization（EBCS） algorithm in terms of surviving nodes number and the success ratio of data transmission

Efficient distributed address assignment algorithm based on topology maintenance in ZigBee networks

Adopting the borrowed address algorithm can decrease the orphan nodes in ZigBee networks that use distributed address assignment mechanism （DAAM）. The existing borrowed address algorithms can increase the success rate of address assignment, but they have defects such as greater cost of overhead and time in founding network caused by breaking topology. To solve such problems, we propose an more efficient distributed borrowed address assignment algorithm based on topology maintenance （A2BTM） that has a topology maintenance function. It borrows address firstly from the offspring nodes in the same branch for the orphan nodes and replies distributed the request of the borrowed address message immediately, to maintain the network topology and decrease the overhead and time spent on the mechanism of borrowed address. Theoretical and simulation analyses manifest that AZBTM algorithm outperforms DAAM and its improved algorithms in terms of the overhead and time spent in founding network, on the premise of keeping a higher success rate of address assignment. Furthermore, A2BTM can lessen the influence from detour phenomenon efficiently.

High-throughput low-delay MAC protocol for Tera Hertz ultra-high data-rate wireless networks

To address the problems of the present Tera Hertz medium access control（MAC） protocols such as not updating the time slot requests numbers in time, unreasonable superframe structures and not merging time slot requests from the same pair of nodes, high throughput low delay medium access control（HLMAC）, a novel MAC protocol for Tera Hertz ultra-high data-rate wireless networks is proposed. It reduces the data access delay largely with a new superframe structure, from which nodes can get time slot allocation information immediately. The network throughput is also improved with the help of updating time slot requests number and merging time slot requests from the same pair of nodes. The theoretical analysis verifies the effectiveness of HLMAC, and the simulation results show that HLMAC improves the network throughput by 65.7% and decreases the access delay by 30%, as compared to energy and spectrum-aware medium access control（ES-MAC）.

Optimized beaconing rate control for vehicular Ad-hoc networks

In vehicular Ad-hoc networks （VANETs）, beacon message is designed for the purpose of periodically broadcasting the status information （velocity, direction, etc.）, which enable neighbor awareness and support some safety applications. However, under high density scenarios, fixed rate beaconing can cause severe congestion and lower the deliver rate of beacons and other kinds of messages. In this paper, we describe beaconing rate control approach with an one-dimensional Markov model, and based on this, an optimized beacon rate control scheme is proposed which aims to mitigate the congestion and maximize the deliver efficiency of beaconing. Analytical and simulation results show that our proposed scheme can achieve higher adaptability and beaconing efficiency compared with other schemes in various environments.

High efficient multipacket decoding approach for network coding in wireless networks

To improve the performance of transmission by reducing the number of transmission and network overhead of wireless single-hop networks, this paper presents a high efficient multipacket decoding approach for network coding （EMDNC） in wireless networks according to the idea of encoding packets which cannot be decoded and are stored in buffer by receiving nodes, the lost packets can be recovered from these encoded packets. Compared with the network coding wireless broadcasting retransmission （NCWBR）, EMDNC can improve the efficiency of decoding and reduce the number of retransmission and transmission delay. Simulation results reveal that EMDNC can effectively reduce the number of retransmission and network overhead.