17 research outputs found
Distributive Stochastic Learning for Delay-Optimal OFDMA Power and Subband Allocation
In this paper, we consider the distributive queue-aware power and subband
allocation design for a delay-optimal OFDMA uplink system with one base
station, users and independent subbands. Each mobile has an uplink
queue with heterogeneous packet arrivals and delay requirements. We model the
problem as an infinite horizon average reward Markov Decision Problem (MDP)
where the control actions are functions of the instantaneous Channel State
Information (CSI) as well as the joint Queue State Information (QSI). To
address the distributive requirement and the issue of exponential memory
requirement and computational complexity, we approximate the subband allocation
Q-factor by the sum of the per-user subband allocation Q-factor and derive a
distributive online stochastic learning algorithm to estimate the per-user
Q-factor and the Lagrange multipliers (LM) simultaneously and determine the
control actions using an auction mechanism. We show that under the proposed
auction mechanism, the distributive online learning converges almost surely
(with probability 1). For illustration, we apply the proposed distributive
stochastic learning framework to an application example with exponential packet
size distribution. We show that the delay-optimal power control has the {\em
multi-level water-filling} structure where the CSI determines the instantaneous
power allocation and the QSI determines the water-level. The proposed algorithm
has linear signaling overhead and computational complexity ,
which is desirable from an implementation perspective.Comment: To appear in Transactions on Signal Processin
A survey of self organisation in future cellular networks
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks
Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing
By leveraging the concept of mobile edge computing (MEC), massive amount of
data generated by a large number of Internet of Things (IoT) devices could be
offloaded to MEC server at the edge of wireless network for further
computational intensive processing. However, due to the resource constraint of
IoT devices and wireless network, both the communications and computation
resources need to be allocated and scheduled efficiently for better system
performance. In this paper, we propose a joint computation offloading and
multi-user scheduling algorithm for IoT edge computing system to minimize the
long-term average weighted sum of delay and power consumption under stochastic
traffic arrival. We formulate the dynamic optimization problem as an
infinite-horizon average-reward continuous-time Markov decision process (CTMDP)
model. One critical challenge in solving this MDP problem for the multi-user
resource control is the curse-of-dimensionality problem, where the state space
of the MDP model and the computation complexity increase exponentially with the
growing number of users or IoT devices. In order to overcome this challenge, we
use the deep reinforcement learning (RL) techniques and propose a neural
network architecture to approximate the value functions for the post-decision
system states. The designed algorithm to solve the CTMDP problem supports
semi-distributed auction-based implementation, where the IoT devices submit
bids to the BS to make the resource control decisions centrally. Simulation
results show that the proposed algorithm provides significant performance
improvement over the baseline algorithms, and also outperforms the RL
algorithms based on other neural network architectures
Key Lab on Wideband Wireless Communications and Sensor Network Technology of Ministry of Education
A fairness-aware resource allocation scheme in a cooperative orthogonal frequency division multiple (OFDM) network is proposed based on jointly optimizing the subcarrier pairing, power allocation, and channel-user assignment. Compared with traditional OFDM relaying networks, the source is permitted to retransfer the same data transmitted by it in the first time slot, further improving the system capacity performance. The problem which maximizes the energy efficiency (EE) of the system with total power constraint and minimal spectral efficiency constraint is formulated into a mixed-integer nonlinear programming (MINLP) problem which has an intractable complexity in general. The optimization model is simplified into a typical fractional programming problem which is testified to be quasiconcave. Thus we can adopt Dinkelbach method to deal with MINLP problem proposed to achieve the optimal solution. The simulation results show that the joint resource allocation method proposed can achieve an optimal EE performance under the minimum system service rate requirement with a good global convergence