Cross layer adaptive transmission in communication systems

2008 ◽  
Author(s):  
Yuling Zhang ◽  
Dongfeng Yuan ◽  
Tingting Han ◽  
Fuzeng Zhang
Keyword(s):  
Communication Systems ◽  
Adaptive Transmission ◽  
Cross Layer

scholarly journals JSMCRP: Cross-Layer Architecture based Joint-Synchronous MAC and Routing Protocol for Wireless Sensor Network

2021 ◽  
Vol 19 (1) ◽  
pp. 94-113
Author(s):  
Shruti Birur Viswanath ◽  
Thippeswamy Muddenahalli Nagendrappa ◽  
Krishna Rao Venkatesh
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocol ◽  
Communication Systems ◽  
Ieee 802.15.4 ◽  
Dynamic Network ◽  
Link Quality ◽  
Wireless Sensor ◽  
Cross Layer ◽  
Monitoring And Control

The exponential rise in wireless technologies and allied applications has revitalized academia-industries to develop more efficient and economic routing solution to meet Quality-of-Service (QoS) provision. Amongst the major wireless communication systems, Wireless Sensor Network (WSN) is the most sought technology for defense surveillance, healthcare monitoring, industrial monitoring and control, civic and strategic infrastructure surveillance etc. Additionally, the up surge in Internet of Things (IoT) and Machine to Machine (M2M) communication systems too have broadened the horizon for WSNs based communication. However, the parallel increase in communication environment, complex network conditions etc confine efficacy of the classical WSN protocols. Dynamic network and node conditions often force classical protocols to undergo pre-mature link outage, delayed transmission, energy exhaustion and eventual QoS violation. To address such issues enhancing Median Access Control (MAC) and allied routing decision can have vital significance. However major existing efforts either focus on MAC enhancement or routing optimization, even under static WSN topology. Considering it as motive in this paper a highly robust “Cross-layer architecture based Joint-Synchronous MAC and Routing Protocol for WSN communication (JSMCRP)” has been developed. Being cross layer model JSMCRP protocol employs Application Layer, Network Layer, MAC Layer and PHY Layer to perform Network Adaptive MAC scheduling and Dynamic Routing Decision. JSMCRP employs Data Traffic Assessment, Prioritization and Scheduling (DTAPS), Proactive Network Monitoring and Knowledge (PNMK), Dynamic Congestion Index Estimation (DCIE), Adaptive Link Quality, Packet Injection Rate and Cumulative Rank Sensitive Routing Decision (CRSRD) to perform routing decision. Additionally, exploiting dynamic network/node conditions it performs Cognitive MAC scheduling to ensure QoS centric communication over IEEE 802.15.4 protocol stack. JSMCRP exhibited higher PDR, lower PLR and low delay under varying network conditions, suitable for real-time communication over constrained mobile WSN conditions.

scholarly journals Deep-Learning-Aided Cross-Layer Resource Allocation of OFDMA/NOMA Video Communication Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 157730-157740
Author(s):  
Shu-Ming Tseng ◽  
Yung-Fang Chen ◽  
Cheng-Shun Tsai ◽  
Wen-Da Tsai
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Communication Systems ◽  
Video Communication ◽  
Cross Layer

scholarly journals Network Efficient Power Control for Wireless Communication Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Daniel U. Campos-Delgado ◽  
Jose Martin Luna-Rivera ◽  
C. J. Martinez-Sánchez ◽  
Carlos A. Gutierrez ◽  
J. L. Tecpanecatl-Xihuitl
Keyword(s):  
Power Control ◽  
Communication Systems ◽  
Wireless Communication Systems ◽  
Cross Layer ◽  
Outer Loop ◽  
Iterative Search ◽  
Network Characteristics ◽  
Cross Layer Optimization ◽  
Power Resources ◽  
Efficient Power

We introduce a two-loop power control that allows an efficient use of the overall power resources for commercial wireless networks based on cross-layer optimization. This approach maximizes the network’s utility in the outer-loop as a function of the averaged signal to interference-plus-noise ratio (SINR) by considering adaptively the changes in the network characteristics. For this purpose, the concavity property of the utility function was verified with respect to the SINR, and an iterative search was proposed with guaranteed convergence. In addition, the outer-loop is in charge of selecting the detector that minimizes the overall power consumption (transmission and detection). Next the inner-loop implements a feedback power control in order to achieve the optimal SINR in the transmissions despite channel variations and roundtrip delays. In our proposal, the utility maximization process and detector selection and feedback power control are decoupled problems, and as a result, these strategies are implemented at two different time scales in the two-loop framework. Simulation results show that substantial utility gains may be achieved by improving the power management in the wireless network.

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