Long-term evolution (LTE) of the UMTS TerrestrialRadio Access and Radio Access Network is considered to ensure the competitiveness of 3GPP radio-access technology in a longer time frame. To minimize/optimize the user equipment (UE) power consumption, and further to support various services and large amount of data transmissions, a discontinuous reception (DRX) mechanism with adjustable DRX cycles has been adopted in LTE RRC_CONNECTED mode. In this paper, we take an overview of the DRX cycle adjustable feature of the LTE power saving mechanism and further modeling the mechanism with bursty packet data traffic using a semi-Markov process. The analytical results, which are validated against simulation experiments, show that LTE DRX achieves power saving gains over UMTS DRX at the price of prolonging wake-up delay. Based on the analytical model, effects of the DRX parameters on the power saving and wake-up delay performance are also investigated, and the results verify a trade-off relationship between the power saving and wake-up delay performance.

Machine-type communication emerges recently as a solution to cater for ubiquitous connections between a diversity of devices and the public wireless data networks. Long Term Evolution Advanced system introduces machine to machine (M2M) communication as an important part of realising the blueprint of smart cities. To bring M2M communication into reality, the access procedure is a key problem. Massive access requests on the air interface in M2M communication make the communication scenarios significantly different from the traditional communications, which challenges the cellular access mechanism greatly. In this paper, the challenges of applying M2M through Long Term Evolution Advanced system are discussed. We model the access on the air interface as a queuing system. The queuing theory indicates that the system is not stable when the occupation rate is high. The access performance of M2M is evaluated by system level simulation, which shows that the congestion on the air interface is very serious in some application cases. To ease the congestion on the air interface, this paper proposes a bundling transmission scheme.

This paper investigates a computing offloading policy and the allocation of computational resource for multiple user equipments (UEs) in device-to-device (D2D)-aided fog radio access networks (F-RANs). Concerning the dynamically changing wireless environment where the channel state information (CSI) is difficult to predict and know exactly, we formulate the problem of task offloading and resource optimization as a mixed-integer nonlinear programming problem to maximize the total utility of all UEs. Concerning the non-convex property of the formulated problem, we decouple the original problem into two phases to solve. Firstly, a centralized deep reinforcement learning (DRL) algorithm called dueling deep Q-network (DDQN) is utilized to obtain the most suitable offloading mode for each UE. Particularly, to reduce the complexity of the proposed offloading scheme-based DDQN algorithm, a pre-processing procedure is adopted. Then, a distributed deep Q-network (DQN) algorithm based on the training result of the DDQN algorithm is further proposed to allocate the appropriate computational resource for each UE. Combining these two phases, the optimal offloading policy and resource allocation for each UE are finally achieved. Simulation results demonstrate the performance gains of the proposed scheme compared with other existing baseline schemes.

No abstract

No abstract