Effective Static and Adaptive Carrier Sensing for Dense Wireless CSMA Networks

Chau, Chi-Kin; Ho, Ivan Wang‐Hei; Situ, Zhenhui; Liew, Soung Chang; Zhang, Jialiang

doi:10.1109/tmc.2016.2557780

Cited by 25 publications

(22 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this article, we target the MAC layer CCI generated by carrier sensing from the IEEE 802.11 CSMA/CA protocol. Although the CSMA/CA guarantees certain network reliability [27], it also brings the CCI problem, especially in high-density wireless environments [28,29]. Following works surrounds such interference problems.…”

Section: Related Workmentioning

confidence: 99%

Queuing Theory Based Co-Channel Interference Analysis Approach for High-Density Wireless Local Area Networks

Zhang

Han

Qian

2016

Sensors

View full text Add to dashboard Cite

Increased co-channel interference (CCI) in wireless local area networks (WLANs) is bringing serious resource constraints to today’s high-density wireless environments. CCI in IEEE 802.11-based networks is inevitable due to the nature of the carrier sensing mechanism however can be reduced by resource optimization approaches. That means the CCI analysis is basic, but also crucial for an efficient resource management. In this article, we present a novel CCI analysis approach based on the queuing theory, which considers the randomness of end users’ behavior and the irregularity and complexity of network traffic in high-density WLANs that adopts the M/M/c queuing model for CCI analysis. Most of the CCIs occur when multiple networks overlap and trigger channel contentions; therefore, we use the ratio of signal-overlapped areas to signal coverage as a probabilistic factor to the queuing model to analyze the CCI impacts in highly overlapped WLANs. With the queuing model, we perform simulations to see how the CCI influences the quality of service (QoS) in high-density WLANs.

show abstract

Section: Related Workmentioning

confidence: 99%

Queuing Theory Based Co-Channel Interference Analysis Approach for High-Density Wireless Local Area Networks

Zhang

Han

Qian

2016

Sensors

View full text Add to dashboard Cite

show abstract

“…CSMA/CA mechanism ensures that there is no interference in the carrier detection range by setting a safe carrier sensing distance and forming a repulsion area [6]. In the CSMA/CA mechanism, the effective way to overcome cumulative interference is to optimize the carrier sensing distance, but the safe carrier sensing range will be larger if the node density increases, and extremely large distance will lead to reduction of resource efficiency, while the design of adaptive interception mechanism will be more complex [7], [8]. In RTS/CTS mechanism, the RTS frames silence neighbor nodes of the sending node by using the maximum transmission power, and the CTS frames silence all receiving nodes to eliminate collisions.…”

Section: Introductionmentioning

confidence: 99%

Research on Resource Efficiency Optimization Model of TDMA-Based Distributed Wireless Ad Hoc Networks

Tong

Liu

2020

IEEE Access

View full text Add to dashboard Cite

Because of networking flexibility, strong robustness and expansibility, and low cost of operation and maintenance, distributed wireless ad hoc networks are widely used in ultra-density 5th-Generation (5G) networks, Device to Device (D2D) communications and industrial wireless sensor networks. However, compared with the centralized ones, the Time Division Multiple Access(TDMA)-based distributed network lacks centralized coordination by a control center, while its resource scheduling only depends on partial information, and the high receiving failure probability caused by neighbors' cumulative interference outside the scope of maintenance may reduce the resource efficiency and restrict its usability according to the existing protocols severely. The simulation results show that the transmission failure probability can be up to 20% when maintaining two-hops neighbors in TDMA-based coordinated distributed scheduling. But the research on the cumulative interference outside the scope of maintenance and the optimal neighbor maintenance scope is still unclear at present. In this paper, we establish a resource efficiency model and an interference model of control messages and data messages under different neighbor maintenance by hardcore point process (HCPP) considering cumulative interference, cost of Media Access Control (MAC) layer and channel multiplex. Furtherly, we build an optimization model to maximize the resource efficiency under scheduling delay and receiving success probability constraints, thus we can obtain the optimal scope of maintenance in different network conditions. We conduct extensive theoretical analysis and simulations to evaluate the correctness of models. Simulation results show that the resource efficiency optimization model can provide guidance for optimal protocol parameters design.

show abstract

“…Research on CSMA/CA has a long tradition for years, on which a node senses the channel before transmitting on a shared transmission medium to avoid collision of data in wireless networks [10]. Recent papers [11,12,13,14] proposed various CSMA/CA scheduling algorithms that are able to optimize network QoS metrics, particularly the network throughput. In [11], a CSMA scheme was formulated in which throughput and power consumption of each node were optimized by controlling back-off and sleeping timers, while ensuring throughput optimality.…”

Section: Introductionmentioning

confidence: 99%

“…In [13], the performance of CSMA network’s throughput was studied under the signal to interference and noise ratio (SINR) model, where a packet was received as long as a certain SINR threshold was exceeded. In [14], they provided effective carrier sensing threshold adjustment algorithms for large wireless CSMA networks. Simulation for evaluating consistency and goodput guaranteed safe interference.…”

Section: Introductionmentioning

confidence: 99%

Aggregated Throughput Prediction for Collated Massive Machine-Type Communications in 5G Wireless Networks

Aly

ElAttar

El-Badawy

et al. 2019

Sensors

View full text Add to dashboard Cite

The demand for extensive data rates in dense-traffic wireless networks has expanded and needs proper controlling schemes. The fifth generation of mobile communications (5G) will accommodate these massive communications, such as massive Machine Type Communications (mMTC), which is considered to be one of its top services. To achieve optimal throughput, which is considered a mandatory quality of service (QoS) metric, the carrier sense multiple access (CSMA) transmission attempt rate needs optimization. As the gradient descent algorithms consume a long time to converge, an approximation technique that distributes a dense global network into local neighborhoods that are less complex than the global ones is presented in this paper. Newton’s method of optimization was used to achieve fast convergence rates, thus, obtaining optimal throughput. The convergence rate depended only on the size of the local networks instead of global dense ones. Additionally, polynomial interpolation was used to estimate the average throughput of the network as a function of the number of nodes and target service rates. Three-dimensional planes of the average throughput were presented to give a profound description to network’s performance. The fast convergence time of the proposed model and its lower complexity are more practical than the previous gradient descent algorithm.

show abstract

Effective Static and Adaptive Carrier Sensing for Dense Wireless CSMA Networks

Cited by 25 publications

References 24 publications

Queuing Theory Based Co-Channel Interference Analysis Approach for High-Density Wireless Local Area Networks

Queuing Theory Based Co-Channel Interference Analysis Approach for High-Density Wireless Local Area Networks

Research on Resource Efficiency Optimization Model of TDMA-Based Distributed Wireless Ad Hoc Networks

Aggregated Throughput Prediction for Collated Massive Machine-Type Communications in 5G Wireless Networks

Contact Info

Product

Resources

About