Frequency planning optimisation in real mobile networks

Due to the annual increase of cellular subscribers, there is a growing interest by the network operators, in how to deploy the network infrastructure to achieve maximum capacity. A key point is the channel or frequency assignment, which implies efficiently assigning frequencies from a limited set, to each cell, while satisfying the electromagnetic compatibility constraints.Peer ReviewedPostprint (published version

Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks

Channel assignments using constrained greedy algorithm, T-coloring and simulated annealing in mesh and cellular networks

Channel assignment is an important step in communication networks. The objectives of minimizing networks interference and the channels used are the problems in the channel assignments of the networks. In real environments, some difference will be expected in the performance of the networks when the channel allocation algorithms under more accurate interference models are deployed. In this research, the wireless mesh networks represent dynamic networks while static networks are represented by the cellular networks. In the wireless mesh networks, communication between a pair of nodes happens when both nodes are assigned with channels. The cellular networks are the radio network distributed over land areas called cells, each served by at least one fixed-location transceiver. Channel assignments in the networks is an application of the vertex coloring in graph theory. Previously, the Greedy Algorithm was used for link scheduling but only the adjacent channel constraint was considered. Here, an algorithm called Improved Greedy Algorithm was proposed to solve the channel assignments by considering the adjacent channel and co-channel constraints which is an improvement to the algorithm. Besides, Simulated Annealing and T-coloring problem are combined to minimize the channels used. The algorithms are applied for single and multiple channels communications in the wireless mesh networks and cellular networks to show the different results of the channel assignments. Further improvement is made on the multiple channels case where the Improved Greedy Algorithm is applied by considering the cosite constraint in addition to the co-channel and adjacent channel constraints. The Improved Greedy Algorithm has been tested in a series of simulations. Results for the simulations prove that the Improved Greedy Algorithm perform significantly well for the channel assignment problem

Energy Efficient Scheduling for Loss Tolerant IoT Applications with Uninformed Transmitter

In this work we investigate energy efficient packet scheduling problem for the loss tolerant applications. We consider slow fading channel for a point to point connection with no channel state information at the transmitter side (CSIT). In the absence of CSIT, the slow fading channel has an outage probability associated with every transmit power. As a function of data loss tolerance parameters and peak power constraints, we formulate an optimization problem to minimize the average transmit energy for the user equipment (UE). The optimization problem is not convex and we use stochastic optimization technique to solve the problem. The numerical results quantify the effect of different system parameters on average transmit power and show significant power savings for the loss tolerant applications.Comment: Published in ICC 201

Maximizing Energy Efficiency in Multiple Access Channels by Exploiting Packet Dropping and Transmitter Buffering

Quality of service (QoS) for a network is characterized in terms of various parameters specifying packet delay and loss tolerance requirements for the application. The unpredictable nature of the wireless channel demands for application of certain mechanisms to meet the QoS requirements. Traditionally, medium access control (MAC) and network layers perform these tasks. However, these mechanisms do not take (fading) channel conditions into account. In this paper, we investigate the problem using cross layer techniques where information flow and joint optimization of higher and physical layer is permitted. We propose a scheduling scheme to optimize the energy consumption of a multiuser multi-access system such that QoS constraints in terms of packet loss are fulfilled while the system is able to maximize the advantages emerging from multiuser diversity. Specifically, this work focuses on modeling and analyzing the effects of packet buffering capabilities of the transmitter on the system energy for a packet loss tolerant application. We discuss low complexity schemes which show comparable performance to the proposed scheme. The numerical evaluation reveals useful insights about the coupling effects of different QoS parameters on the system energy consumption and validates our analytical results.Comment: in IEEE trans. Wireless communications, 201