Data Mining Practical Machine Learning Tools and Techniques

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Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.

Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial

Cooperation between the fog and cloud in fog-enabled radio access network could enormously improve the computation offloading services both in terms of latency and energy consumption, while edge caching allowing for low-latency transmission of the cached files, is essential for cooperation. It is necessary to select the best method that minimizes the energy consumption with latency tolerance guaranteed, to offload the task. In this paper, we study the problem of content placement for edge caching and the cooperative computation offloading between the fog and the cloud by optimizing the cooperative offloading decisions and caching strategies. The problem is formulated to minimize the total energy consumption of all the UEs by optimizing the transmit power of the user equipments (UEs), caching strategies and the cooperation factor (the ratio of the fog execution of a file to the whole file), which is a non-convex programming problem. An approximate solution is obtained by using successive convex approximation strategy and implicit enumeration method. Numerical results show that our proposed algorithm can achieve better performance compared with other schemes, e.g., cloud computing and cache most popular strategies.

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Edge Caching and Computation Offloading for Fog-Enabled Radio Access Network

In this paper, we investigate dynamic resource allocation (DRA) problems for Internet of Things (IoT) in real-time cloud radio access networks (C-RANs), by combining gradient boosting approximation and deep reinforcement learning to solve the following two major problems. Firstly, in C-RANs, the decision making process of resource allocation is time-consuming and computational-expensive, motivating us to use an approximation method, i.e. the gradient boosting decision tree (GBDT) to approximate the solutions of second order cone programming (SOCP) problem. Moreover, considering the innumerable states in real-time C-RAN systems, we employ a deep reinforcement learning framework, i.e., deep Q-network (DQN) to generate a robust policy that controls the status of remote radio heads (RRHs). We propose a GBDT-based DQN framework for the DRA problem, where the heavy computation to solve SOCP problems is cut down and great power consumption is saved in the whole C-RAN system. We demonstrate that the generated policy is error-tolerant even the gradient boosting regression may not be strictly subject to the constraints of the original problem. Comparisons between the proposed method and existing baseline methods confirm the advantages of our method.

Resource Allocation Using Gradient Boosting Aided Deep Q-Network for IoT in C-RANs

In this paper, the sum rate is maximized for the intelligent reflective surface (IRS) assisted terahertz (THz) non-orthogonal multiple access (NOMA) communication system. A novel algorithm is proposed to alternatively optimize the IRS phase shift, the sub-band allocation, and power control. To tackle the formulated non-convex problem, we utilize the auxiliary variables to find the feasible initialization solution meanwhile guarantee the individual rate requirements. The decoding order of successive interference cancellation (SIC) is determined according to channel gain maximization, and the IRS phase is further adjusted to improve the sum rate. A long-distance priority (LDP) algorithm is then proposed to compensate for the distance-dependent THz pathloss attenuation, and a blocking pair eliminating (BPE) algorithm is proposed to obtain a stable THz sub-band allocation. Simulation results show that the proposed scheme significantly enhances the sum-rate performance of the IRS-assisted THz NOMA networks.

Intelligent Reflecting Surfaces-Supported Terahertz NOMA Communications

Utilizing the inherent sparsity of massive multiple-input multiple-output (MIMO) channels in the beam domain, we propose a bipartite graph approach for frequency-division duplexing (FDD) vehicle-to-vehicle (V2V) underlay massive MIMO transmission. First, the physically motivated constraints are introduced to schedule the users with channel dimension no larger than the pilot dimension and beam directions with strong channel power as well as weak interference. We then develop an optimization problem which is formulated as a mixed-integer linear program (MILP) to maximize the rank of the effective channel matrix subject to the introduced constraints. We further provide a channel estimation and precoding scheme for the base station and each V2V transmitter over the equivalent reduced-dimensional channels based on the solution of MILP. Numerical results show the superiority of the proposed bipartite graph approach in terms of pilot overhead and spectral efficiency over the conventional baseline.

A Bipartite Graph Approach for FDD V2V Underlay Massive MIMO Transmission

Energy detection is widely used in cognitive radio due to its low complexity. One fundamental challenge is that its performance degrades in the presence of noise uncertainty, which inevitably occurs in practical implementations. In this work, three novel detectors based on uniformly distributed noise uncertainty as the worst-case scenario are proposed. Numerical results show that the new detectors outperform the conventional energy detector with considerable performance gains.

Kezhi Wang

Papers

Edge Caching and Computation Offloading for Fog-Enabled Radio Access Network

Resource Allocation Using Gradient Boosting Aided Deep Q-Network for IoT in C-RANs

Intelligent Reflecting Surfaces-Supported Terahertz NOMA Communications

A Bipartite Graph Approach for FDD V2V Underlay Massive MIMO Transmission

Novel energy detection using uniform noise distribution