Showing papers by "Daquan Feng published in 2019"

Intelligent Offloading in Multi-Access Edge Computing: A State-of-the-Art Review and Framework

Abstract: Multi-access edge computing (MEC), which is deployed in the proximity area of the mobile user side as a supplement to the traditional remote cloud center, has been regarded as a promising technique for 5G heterogeneous networks. With the assistance of MEC, mobile users can access computing resource effectively. Also, congestion in the core network can be alleviated by offloading. To adapt in stochastic and constantly varying environments, augmented intelligence (AI) is introduced in MEC for intelligent decision making. For this reason, several recent works have focused on intelligent offloading in MEC to harvest its potential benefits. Therefore, machine learning (ML)-based approaches, including reinforcement learning, supervised/unsupervised learning, deep learning, as well as deep reinforcement learning for AI in MEC have become hot topics. However, many technical challenges still remain to be addressed for AI in MEC. In this article, the basic concept of MEC and main applications are introduced, and existing fundamental works using various ML-based approaches are reviewed. Furthermore, some potential issues of AI in MEC for future work are discussed.

Direct Acyclic Graph based Ledger for Internet of Things: Performance and Security Analysis.

Abstract: Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.

Computation Offloading for Mobile Edge Computing Enabled Vehicular Networks

Abstract: The emergence of computation-intensive and delay-sensitive vehicular applications poses a great challenge for individual vehicles with limited computation resources. Mobile edge computing (MEC) is a new paradigm shift that can enhance vehicular services through computation offloading. However, the high mobility of vehicles will affect offloading performance. In this paper, we investigate the vehicular user (VU) computation overhead minimization problem in MEC-enabled vehicular networks by jointly optimizing the computation and communication resources' allocation (transmit power and uploading time for communication, and the offloading ratio and local CPU frequency for computation). This optimization problem is nonconvex and difficult to solve directly. To deal with this issue, we first transform the original problem into an equivalent one. Then, we decompose the equivalent problem into a two-level problem. In addition, we develop a low-complexity algorithm to obtain the optimal solution. The numerical results demonstrate that the proposed algorithm can significantly outperform benchmark algorithms in terms of computation overhead.

Toward Ultrareliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues

Abstract: Ultrareliable low-latency communications (URLLC) is one of three emerging application scenarios in 5G new radio (NR) for which physical layer design aspects have been specified. With 5G NR, we can guarantee reliability and latency in radio access networks. However, for communication scenarios where the transmission involves both radio access and wide-area core networks, the delay in radio access networks contributes to only a portion of the end-toend (E2E) delay. In this article, we outline the delay components and packet loss probabilities in typical URLLC scenarios and formulate the constraints on E2E delay and overall packet loss probability. Then, we summarize possible solutions in the physical, link, and network layers as well as the cross-layer design. Finally, we discuss open issues in prediction and communication codesign for URLLC in wide-area, largescale networks.

Energy Efficient V2X-Enabled Communications in Cellular Networks

Abstract: Vehicle-to-everything (V2X) communications, which provide wireless connectivity among vehicles, roadside drivers, passengers, and pedestrians, are attracting great interest. In this paper, we propose an energy-efficient relay assisted transmission scheme based on V2X communications in the uplink cellular networks for delay insensitive applications. We aim to maximize energy efficiency of the uplink while considering both the circuit power and transmit power consumption, as well as the delay constraint. The optimal resource allocation for direct transmission mode and V2X-enabled transmission mode are derived. For the V2X-enabled transmission mode, we first consider the transmission without the delay constraint and obtain the corresponding optimal power allocation. Based on the obtained result, we further propose the optimal resource allocation policy that satisfies the delay constraint and minimizes the energy consumption. Simulation results show that there exists an energy-delay tradeoff and the proposed V2X-enabled scheme can achieve significant energy saving with an acceptable delay compared with the traditional direct scheme, the decoding-and-forward based transmission scheme, and the opportunistic store-carry and forward based transmission scheme, especially when the cell radius is not small.

Towards Ultra-Reliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues

Abstract: Ultra-reliable low-latency communications (URLLC) has been considered as one of the three new application scenarios in the \emph{5th Generation} (5G) \emph {New Radio} (NR), where the physical layer design aspects have been specified. With the 5G NR, we can guarantee the reliability and latency in radio access networks. However, for communication scenarios where the transmission involves both radio access and wide area core networks, the delay in radio access networks only contributes to part of the \emph{end-to-end} (E2E) delay. In this paper, we outline the delay components and packet loss probabilities in typical communication scenarios of URLLC, and formulate the constraints on E2E delay and overall packet loss probability. Then, we summarize possible solutions in the physical layer, the link layer, the network layer, and the cross-layer design, respectively. Finally, we discuss the open issues in prediction and communication co-design for URLLC in wide area large scale networks.

Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems

Abstract: In this paper, we consider the combination of machine learning (ML) and wireless communication. We design a machine learning generated clusters model in a distributed antenna system (DAS), which is constructed by two different ML clustering algorithms, i.e., k-means algorithm and Gaussian mixture model-based (GMM) algorithm. Under the communication scenario of DAS with ML generated clusters model, we investigate two different power allocation optimization problems with the interference of maximizing spectral efficiency (SE) and energy efficiency (EE) in DAS, respectively. We compare the SE and EE of DAS with ML generated clusters model and the conventional model. The simulation results verify the effectiveness of DAS with ML generated clusters model, which can obtain the much better performance of SE and EE compared with the conventional communication model in DAS.

Joint Time and Power Allocation in Multi-Cell Wireless Powered Communication Networks

Abstract: This paper investigates the joint time and power allocation in a multi-cell wireless powered communication network (WPCN) with successive interference cancellation. In the WPCN, the hybrid access points broadcast wireless energy to all users and then the users utilize the harvested energy to transmit information simultaneously in a spectrum-sharing fashion. The max-min fairness throughput optimization and max-sum throughput optimization problems are formulated to analyze the system performance. However, both of the two optimization problems are nonconvex due to the complicated co-channel interference and the coupled time and power variables. To deal with this challenge, efficient two-stage approaches are proposed to transform them into more tractable ones. Specifically, for the max-min fairness throughput optimization problem, we first convert the power allocation problem into geometric programming (GP) problem for given time allocation and then optimize the time allocation. For the max-sum throughput optimization problem, we first transform the power allocation problem into a signomial programming problem with fixed time allocation, which is further approximated as a GP one. Then, time allocation is optimized. The simulation results validate the effectiveness of the two proposed approaches. In addition, it is shown that the “doubly near-far” issue can be well addressed by the max-min fairness throughput optimization.

Optimal Time Allocation in Multi-Cell Wireless Powered Communication Networks

Abstract: This paper focuses on time allocation to maximize the minimum throughput and sum-throughput of all users in a multi-cell wireless powered communication network (WPCN). Since interference of multi-cell WPCN is difficult to be calculated, we consider a multi-cell WPCN with load coupling and long-term average interference at all hybrid access points. The problems of maximizing the minimum throughput and sum-throughput are non-convex. First, two efficient optimization approaches are proposed to transform them into convex ones. As the max-min fairness throughput scheme cares more about the worst-case user, the doubly near-far problem can be addressed by the minimum throughput maximation. We also demonstrate the effectiveness of the proposed approaches through numerical analysis.

Impact of Network Load on Direct Acyclic Graph Based Blockchain for Internet of Things

Abstract: In this paper, we investigate the impact of network load on the Direct Acyclic Graph (DAG) consensus process for the blockchain enabled Internet of Things. Specifically, Markov chain model has been adopted to illustrate the dynamics in the consensus process for DAG based blockchain networks. The key performance metrics including cumulative weight and transaction confirmation delay are analysed under the unsteady state caused by the fluctuation of transaction arrival rate. The analysis is based on one of the most typical DAGs, Tangle, and the results show that the performance will be affected when the transaction arrival rate fluctuates widely.

Power-Spectrum Trading for Full-Duplex D2D Communications

Abstract: Device-to-device (D2D) communications has been considered as a promising technique that enables adjacent mobile terminals to transmit point-to-point data directly without going through base stations. Since two-way data exchanging is common in practical D2D communications scenarios, full-duplex (FD) communications can be exploited to improve the performance of D2D communications. Therefore, to a certain extent, efficient resource allocation to avoid severe self-interference of FD communications and the interference between D2D users (DUs) and cellular users (CUs) is critical to fully realize the potential benefit of FD-enabled D2D communications. In this paper, we investigate the resource allocation for a FD D2D pair coexisting with a cell-edge CU. By exploiting the asymmetry of spectrum and energy resources between the DU and CU, we propose a spectrum and energy trading method to achieve the mutual benefits of both users. Concave-convex procedure (CCCP) algorithm is adopted to solve the optimization problem. Simulation results show that the proposed method can significantly improve the system performance.

Method, device, equipment and storage medium for calculating throughput of multi-cell wpcn

Abstract: The invention discloses a throughput calculation method, a device, equipment and a storage medium of a multi-cell wireless charging communication network. The method comprises the following steps: broadcasting wireless energy to a user terminal in the wireless charging communication network through a cell mixed access point; receiving the preset information sent by the subscriber using the wireless energy by the cell hybrid access point; calculating a throughput when the cell hybrid access point receives the preset information by using a time period during which the cell hybrid access point receives the preset information. Compared with the prior art, the invention can calculate the throughput of the cell hybrid access point when the cell hybrid access point receives the preset informationby using the time period of the cell hybrid access point receiving the preset information, and the calculated throughput can be used for solving the double near-far effect problem existing in the multi-cell wireless charging communication network.

Method and apparatus for power allocation in distributed antenna system with added d2d communication

Abstract: The present invention is suitable for use in the technical field of base station communication, and provides a method for power allocation in a distributed antenna system DAS with added D2D communication, specifically providing a method for optimal power allocation for use when maximising spectral efficiency and maximising energy efficiency, the power allocation method comprising: initialising iterative parameters in a sub-gradient iterative algorithm and initialising a transmission power formula (I) and pd; on the basis of the formula of the sub-gradient iterative algorithm combined with the iterative parameters, calculating formula (I) and pd; on the basis of the formula of a Lagrange multiplier iterative algorithm, updating the iterative parameters to obtain updated iterative parameters; if the updated iterative parameters all converge, then formula (I) and pd calculated on the basis of the converging iterative parameters are the optimal power, and the iteration operation ends; if not, then returning to continue calculating formula (I) and pd and continuing to update the iterative parameters. The power allocation method provided in the present invention combines D2D communication with DAS to make full use of the advantages of both, improving communication quality of the communication cell and reducing the energy consumption of the cell.