Efficient Interference-Aware Power Control for Wireless Networks

Yao, Junmei; Lou, Wei; Yang, Chao; Wu, Kaishun

doi:10.1016/j.comnet.2018.02.017

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…In this, as the PDR increases, the throughput also gets increased. Here, Basic Power Control mechanism (BPC) and Adaptive Range‐based Power Control (ARPC) are designed only to circumvent interference and not for concurrent transmissions of lowest throughput distances from 100 and 400 m. Unlike the others, such as RCSS‐MAC, Interference Resistant Multiple Access (IRMA), Interference‐Aware Power Control (IAPC), and Signal to Interference plus Noise Ratio (SINR) based Transmission Power Control (STPC) are designed for concurrent transmissions. Comparing with IAPC and ARPC, RCSS‐MAC protocol achieves 13.5% and 17% throughput enhancement when d is 100 and 400 m. Compared with STPC, RCSS‐MAC achieves 35% throughput improvement when d = 100 m and 53% improved when d = 400 m. Throughput can be minimized due to concurrent transmission.…”

Section: Experimental Setup and Analysismentioning

confidence: 99%

Capacity utilization based on contention window management in mobile ad hoc network

M¹,

Saleh

2019

Int J Numerical Modelling

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A major challenge to the mobile ad hoc network (MANET) is the ability to efficiently utilize the available capacity. The exposed terminal problem (ETP) and the hidden terminal problem (HTP) are considered as major concerns in the media access control (MAC) layers of MANET. During transmission, there is an increase in energy consumption, unnecessary delay, overhead, and the number of retransmission due to the request to send (RTS) packet collision. To eliminate these problems, we have developed an algorithm called receiver collision sensing scheme in multiple access control (RCSS‐MAC) for collisions detection by the receiver. This method uses appeal for RTS (ARTS) packet to detect unsuccessful RTS packet. Here, we use only the minimal number of ARTS to reduce unnecessary retransmission, delay, and RTS collision. The performance of this method can be improved using the contention window (CW) management scheme to reset and renovate the failed system. In RCSS‐MAC, an additional field (victory rate) in MAC frame is utilized to control the CW management to reflect the network condition in and around the receiver. Simulation is carried out under the open‐source second version of the network simulator (NS2). As a result, RCSS‐MAC algorithm achieves better performance than conventional techniques in terms of delay, throughput, and energy consumption and network lifetime.

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Section: Experimental Setup and Analysismentioning

confidence: 99%

Capacity utilization based on contention window management in mobile ad hoc network

M¹,

Saleh

2019

Int J Numerical Modelling

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“…More recently, the adoption of Universal Software Radio Peripherals (USRP) dedicated devices [12] made it possible to improve the accuracy of the interference generation systems to higher levels and has been adopted as a possible solution for testing interference-aware power control mechanisms [13], [14]. However, they currently represent a too costly solution because of the need of specialized hardware, which makes it difficult an adoption on large scale testbeds.…”

Section: Related Workmentioning

confidence: 99%

In-Band Controllable Radio Interference Generation for Wireless Sensor Networks

Lattanzi

Freschi

2019

IEEE Access

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Interference signals negatively impact the performance of wireless embedded systems. The increased packet losses, delays, and energy consumption experienced by devices operating in environments subject to interference are particularly critical in constrained systems such as wireless sensor networks. The need to design and test systems for mitigating the effects of interference prompts for the capability of reproducing in a controllable way suitable interference signals. Solutions have been proposed recently, which tackle the problem by making use of 802.15.4 compliant radio transceivers, like those available on board of commonly used sensor nodes, thus paving the way for low cost and repeatable generation of interference which could reliably emulate real-world scenarios, for instance densely deployed networks. In this paper, we present an investigation regarding the emulation of interference sources by means of 802.15.4 radios on novel 32-bit wireless system-on-chip. The study is based on an extensive experimental evaluation, providing novel insights into the main features of the system. In particular, the effects of interference on the communication link, measured in terms of packet reception rate, are investigated for different parameters (namely, duty cycle and power of the interference signal, communication protocols, and payload size), and results are discussed concerning the feasibility of emulating background noise by means of the analyzed techniques.

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“…In order to avoid the interference produced by adjacent APs and users, the APs can turn to idle channel to reduce co-channel interference [8], [11]. Moreover, power control schemes have been introduced to adjust the coverage area and transmit signal strength to improve throughput [12], [13]. Existing power control schemes are often approach coverage problems by increasing power, which leads to detrimental results (i.e.,interference, delay).…”

Section: Introductionmentioning

confidence: 99%

Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning

Zhao

et al. 2019

IEEE Access

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In dense Wireless Local Area Networks (WLANs), high-density Access Points (APs) bring severe interference that seriously affects the experience of users, resulting in lower throughput and poor connection quality. Due to the heavy computation workload raised by the sizable networking systems and the difficulty in estimating instantaneous Channel State Information (CSI), existing works are hard to solve interference problem. In this paper, we propose a Joint Power control and Channel allocation based on Reinforcement Learning (JPCRL) algorithm combining with statistical CSI to reduce interference adaptively. Firstly, we analyze the correlation between transmit power and channel, and formulate the interference optimization as a Mixed Integer Nonlinear Programming (MINLP) problem. Secondly, we use the statistical CSI method to take the power and channel state as the state and action space, the overall throughput increment as the reward function of Q-learning, and obtain the optimal joint optimization strategy through off-line training. Moreover, for the periodic reinforcement learning process leading to resource consumption, we design an event-driven mechanism of Q-learning, which triggers online learning to refresh the optimal policy by event-driven condition and the consumption of computing resources can be reduced. The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing scheme. INDEX TERMS Interference, throughput, reinforcement learning, channel allocation, power control.

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Efficient Interference-Aware Power Control for Wireless Networks

Cited by 10 publications

References 13 publications

Capacity utilization based on contention window management in mobile ad hoc network

Capacity utilization based on contention window management in mobile ad hoc network

In-Band Controllable Radio Interference Generation for Wireless Sensor Networks

Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning

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