Abstract

Machine to machine (M2M) communication has received increasing attention in recent years. A M2M network exhibits salient features such as large number of machines/devices, low data rates, delay tolerant/sensitive, small sized packets, energy-constrained and low or no mobility. A large number of M2M terminals may exist in a small area with many trying to simultaneously and randomly access for channel resources - which will result in overload and access problem. This increased signaling overhead and diverse requirements of machine type communication devices (MTCDs) call for the development of flexible and efficient scheduling and random access techniques. In this thesis, we first review and compare various scheduling and random access techniques in LTE-based cellular networks for M2M communication. We also discuss how successful they are to fulfill the unique requirements of M2M communication and networking. Resource management in M2M networks with a large number devices is also reviewed from the access point of view. We propose a multi-objective optimization based solution to the problem of resource allocation in interference-limited M2M communication. We consider MTCDs in a clustered network structure, where they are divided into clusters and the devices belonging to a cluster communicate to cluster head (or controller). We maximize the number of admitted MTCD controllers and throughput with least interference caused to conventional primary users. We formulate the problem as a mixed-integer non-linear problem with multiple objectives and solve it using meshed adaptive direct search (MADS) algorithm. Simulation results show the effects of varying different parameters on cumulative throughput and the number of admitted iii MTCD controllers. We then formulate the slot selection problem in M2M networks with admitted MTCDs as an optimization problem. We present a solution using the Q-learning algorithm to select conflict-free slot assignment in a random access network with MTCD controllers. The performance of the solution is dependent on parameters such as learning rate and reward. We thoroughly analyze the performance of the proposed algorithm considering different parameters related to its operation. We also compare it with simple ALOHA and channel-based scheduled allocation and show that the proposed Q-learning based technique has a higher probability of assigning slots compared to these techniques. We then present a block based Q-learning algorithm for the scheduling of MTCDs in clustered M2M communication networks. At first centralized slot assignment is done and an algorithm is proposed for minimizing the inter-cluster interference. Then we propose to use an Q-learning algorithm to assign slots in a distributed manner and comparison is made between the two schemes. Afterwards, we show the effects of distributed slot-assignment with respect to varying signal-to-interference ratio on convergence rate and convergence probability. Cumulative distribution function is used to study the effect of various SIR threshold levels on the convergence probability. With the increase in SIR threshold levels, increase in convergence time and decrease in convergence probability are observed, as less block configuration fulfills the required threshold in the M2M network.

Highlights

  • Mobile communication systems have evolved from supporting analog voice only to rich multimedia services poviding hundreds of thousands of different applications to billions of users

  • When a device switches on or comes out of idle mode to get connected to the network, the device does not have any resource or channel available to inform the network about its desire to connect to it, so it will send its request over a shared medium; this process is called random access, done via random access channel (RACH) in LTE

  • We considered a clustered network consisting of H2H (HDs) and machine type communication devices (MTCDs), where all MTCDs communicate with a cluster head

Read more

Summary

Introduction

In a typical machine to machine (M2M) network, large number of MTCDs exist and they mostly transmit a small amount of data. Distributed allocation performs well in terms of computational complexity and time, reduced network traffic and no involvement of evolved node base station (eNB). We deal with the problem of slot assignment in random access network (RAN) of MTCDs in a clustered network and present a strategy for selection of time slots in a frame by controllers in TDMA-based M2M networks. Our work in comparison with [141, 142, 156] is novel in a sense that we reduce the computational complexity and time by introducing the concept of block-based or clustered slot allocation using Q-learning. A centralized algorithm is proposed for slot allocation for MTCDs such that minimum inter-cluster interference is experienced by all the devices. This is based on graph colouring mechanism.

M2M Communication
Reinforcement Learning Approach
Resource Allocation in M2M Networks
Thesis Contribution
Thesis Organization
Scheduling in M2M Communication
Channel Awareness in Scheduling
Delay Dependent Scheduling
QoS Based Group Scheduling
Efficient Spectrum Utilization
Random Access
Random Access in MTC Devices
Access Delay
Traffic Overload and Congestion Control
Resource Allocation
Energy-Efficient Algorithms
Admission and Rate Control
Prospective Scheduling and Random Access Techniques
Chapter Summary
System Model
Problem Formulation and Solution
Proposed Solution
6: Algorithm Execution
Simulation Setup
Results and Discussion
Clustered M2M Network
Slot/Channel Allocation
Formation of Clusters
Problem Formulation
Single Frequency Case
Multiple Frequency Case
Classic Techniques
Reinforcement learning
Q-Learning Algorithm for M2M Communication
Results and Discussions
Effect of Learning Rate α
Effect of Reward R
Effect of Both α and R
Q-Learning versus RA/CBA
Centralized Slot Allocation
Select Results
Distributed Slot Allocation
Numerical Results
Centralized vs Distributed Implementation
Distributed Implementation
Conclusions and Future work
Conclusion
Future Work

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.