Joint Optimization of Clustering and Scheduling for Machine-to-Machine Communications in Cellular Wireless Networks

Tsai, Yun-Da; Song, Chang-Yu; Hsieh, Hung‐Yun

doi:10.1109/vtcspring.2015.7145901

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…Applying data aggregation is one way to reduce energy consumption by reducing transmission overhead and prolonging the lifetime of M2M network. Proceeding with the same idea, various data aggregation schemes are proposed in [13,[100][101][102]. In [101], two methods based on the Karhunen Loe (KLT) or partial KLT transform for data aggregation in M2M gateways are proposed.…”

Section: Data Aggregationmentioning

confidence: 99%

“…Upon its expiration, a M2M device retrieves data collected in the buffering period and aggregates them into a new message, which increases the transmission delay from the source to the application server. To address this issue, authors proposed an analytical model in [102] to investigate the configuration on buffering. Authors discussed the performance metrics related to latency of the application server, aggregated volume and energy consumption against the buffering time configured in MTC devices.…”

Section: Data Aggregationmentioning

confidence: 99%

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

Hussain¹

2021

Preprint

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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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Section: Data Aggregationmentioning

confidence: 99%

Section: Data Aggregationmentioning

confidence: 99%

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

Hussain¹

2021

Preprint

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“…Clustering increases the efficiency of data transmission by reducing the number of devices attempting to access the BS. Thus, it leads to reduced signal overheads among the network and save the energy of the whole system [66].…”

Section: Clustering In M2m Communicationsmentioning

confidence: 99%

An Architecture for M2M Communications Over Cellular Networks Using Clustering and Hybrid TDMA-NOMA

Rozario

Hossain

2018

2018 6th International Conference on Information and Communication Technology (ICoICT)

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It is hereby declared that 1. The thesis submitted is my own original work while completing degree at BRAC University.2. The thesis does not contain material previously published or written by a third party, except where this is appropriately cited through full and accurate referencing.3. The thesis does not contain material which has been accepted, or submitted, for any other degree or diploma at a university or other institution.4. I have acknowledged all main sources of help.

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“…Power management of mobile devices and communication networks have been advancing with the growing number of mobile consumer electronics and Internet of Things (IoT) devices. To improve their power efficiency and battery life, multiple levels of engineering efforts have been conducted from system-level optimization [1][2][3][4] to device-level power management such as dynamic voltage and frequency scaling [5][6][7][8]. Digital systems and system-on-chip devices usually have multiple voltage domains that need to be adjusted reflecting dynamic variations of load conditions.…”

Section: Introductionmentioning

confidence: 99%

An Output Capacitor-Less Low-Dropout Regulator with 0–100 mA Wide Load Current Range

Park

Kang

et al. 2019

Energies

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An output capacitor-less low-dropout (OCL-LDO) regulator with a wide range of load currents is proposed in this study. The structure of the proposed regulator is based on the flipped-voltage-follower LDO regulator. The feedback loop of the proposed regulator consists of two stages. The second stage is turned on or off depending on the variation in the output load current. Hence, the regulator can retain a phase margin at a wide range of load currents. The proposed regulator exhibits a better regulation performance compared to the ones in previous studies. The test chip is fabricated using a 65-nm CMOS process.

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Joint Optimization of Clustering and Scheduling for Machine-to-Machine Communications in Cellular Wireless Networks

Cited by 8 publications

References 8 publications

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

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

An Architecture for M2M Communications Over Cellular Networks Using Clustering and Hybrid TDMA-NOMA

An Output Capacitor-Less Low-Dropout Regulator with 0–100 mA Wide Load Current Range

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