Two-Phase Scheduling and Leakage-Based Precoding in Wireless Cellular Networks

Bendlin, Ralf; Brunner, Hans H.; Ivrlač, Michel T.; Nossek, Josef A.; Huang, Yih-Fang

doi:10.1109/twc.2013.061413.120320

“…For a cellular network consisting of multiple eNBs, however, such cooperation of instantaneous CSI is problematic. It requires precise synchronization among eNBs, and estimation of CSI for multiple cells create immense delays and large communication overhead [56].…”

Section: Channel Awareness In Schedulingmentioning

confidence: 99%

Resource allocation in clustered M2M networks: a q-learning approach

Hussain¹

2021

Preprint

1

0

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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.

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“…For a cellular network consisting of multiple eNBs, however, such cooperation of instantaneous CSI is problematic. It requires precise synchronization among eNBs, and estimation of CSI for multiple cells create immense delays and large communication overhead [56].…”

Section: Channel Awareness In Schedulingmentioning

confidence: 99%

Resource allocation in clustered M2M networks: a q-learning approach

Hussain¹

2021

Preprint

0

View full text Add to dashboard Cite

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.

show abstract

“…For a cellular network consisting of multiple eNBs, however, such cooperation of instantaneous CSI is problematic. It requires precise synchronisation amongst eNBs, and estimation of CSI for multiple cells create immense delays and large communication overhead .…”

Section: Scheduling In M2m Communicationmentioning

confidence: 99%

Medium access control techniques in M2M communication: survey and critical review

Hussain

¹

,

Anpalagan

²

,

Vannithamby

³

2014

Trans. Emerging Tel. Tech.

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Machine-to-machine (M2M) communication has received increasing attention in recent year. An M2M network exhibits some salient features such as large number of machines/devices, low data rates, delay tolerant/sensitive, small 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 signalling overhead and diverse requirements of machine-type communication (MTC) devices call for the development of flexible and efficient scheduling and random access techniques. In an M2M scenario, where the network is operating at high offered load with a large number of contending transmitters, distributed random access techniques are more appropriate than centralised scheduling techniques because of less control messages and better channel utilisation. There is a need for comparison of various medium access methods that can be used in the development of an efficient hybrid M2M and human to human network. In this article, we review and compare various scheduling and random access techniques in cellular networks, particularly in Long-Term Evolution. 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 of MTC devices is also discussed from the access point of view. Energy efficiency, being one of the main challenges of quality-of-service-constrained M2M communication, is also discussed. Minimisation of the energy consumption is tightly bound to channel access and hence considered in the comparison of various medium access control protocols. Finally, some potential research directions related to access control and resource allocation are presented for future work.

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Two-Phase Scheduling and Leakage-Based Precoding in Wireless Cellular Networks

Cited by 4 publications

References 68 publications

Resource allocation in clustered M2M networks: a q-learning approach

Resource allocation in clustered M2M networks: a q-learning approach

Resource allocation in clustered M2M networks: a q-learning approach

Medium access control techniques in M2M communication: survey and critical review

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