A cross-layer optimization framework for congestion and power control in cognitive radio ad hoc networks under predictable contact

Abstract

In this paper, we investigate the cross-layer optimization problem of congestion and power control in cognitive radio ad hoc networks (CRANETs) under predictable contact constraint. To measure the uncertainty of contact between any pair of secondary users (SUs), we construct the predictable contact model by attaining the probability distribution of contact. In particular, we propose a distributed cross-layer optimization framework achieving the joint design of hopby-hop congestion control (HHCC) in the transport layer and per-link power control (PLPC) in the physical layer for upstream SUs. The PLPC and the HHCC problems are further formulated as two noncooperative differential game models by taking into account the utility function maximization problem and the linear differential equation constraint with regard to the aggregate power interference to primary users (PUs) and the congestion bid for a bottleneck SU. In addition, we obtain the optimal transmit power and the optimal data rate of upstream SUs by taking advantage of dynamic programming and maximum principle, respectively. The proposed framework can balance transmit power and data rate among upstream SUs while protecting active PUs from excessive interference. Finally, simulation results are presented to demonstrate the effectiveness of the proposed framework for congestion and power control by jointly optimizing the PLPC-HHCC problem simultaneously.

Review on cross‐layer design for cognitive ad‐hoc and sensor network

“…Paper Year Network type Cross-layers parameters Observations [16] 2012 CR network physical layer: power allocation, AMC this design demonstrated effective improvement in TCP throughput, as well as increase in average network lifetime MAC layer: frame size functionalities transport layer: best relay selection [17] 2016 CRAHNs physical layer: spectrum sensing this improved design eliminated the frequent slow start and mostly had a large TCP congestion window which enhanced overall the transmission efficiency MAC layer: channel switching transport layer: TCP-improved algorithm [18] 2010 CR networks physical layer: spectrum sensing, modulation and coding, access decision low-layer design parameters can significantly impact on the TCP throughput over CR networks, which were improved through cross-layer approach in their work MAC layer: frame size transport layer: TCP [19] 2013 IEEE 802.22 WRAN based CR networks physical layer: spectrum sensing proposed solutions perform close interaction between the transport and MAC/PHY layers to enhance TCP performance MAC layer: scheduling transport layer: TCP [20] 2015 CR network MAC layer: resource allocation demonstrated higher traffic load and lower delay for CR network network layer: routing [21] 2015 CRAHNs MAC layer: scheduling and random channel access high utilisation of unused spectrum by SU and throughput maximisation among coexisting peer nodes network layer: routing [22] 2017 CRAHNs MAC layer: TDMA scheduling collision among SUs is avoided by incorporating distributed TDMA MAC and location aware forwarding protocol network layer: channel selection [23] 2012 CRAHSN physical + MAC layer: modulation, channel coding, propagation, power transmission, channel estimation, hole detection, horizontal handover, hardware temperature monitoring reduced energy consumption by CR nodes and optimisation of spectrum resources network + transport layer: standards recognition, vertical handover, inter/ intra network handover, link load analysis application layer: user profile attribute control (sound, video, speed, indoor/outdoor, operator etc.) [43] 2018 CRAHNs physical layer: power control achieved optimal transmit power and optimal data rate for upstream SUs transport layer: congestion control, rate control [44] 2018 underlay CRN physical layer: dynamic spectrum access, SINR achieved improved video quality and efficient load balancing of transmitted packets network layer: routing, queue management, resource allocation IET Commun., 2020, Vol. 14 Iss.…”

Dynamic Power Optimization for Secondary Wearable Biosensors in E-Healthcare Leveraging Cognitive WBSNs with Imperfect Spectrum Sensing

“…where ξ 1 is the constant number. By substituting (31), (32), and (35) into (A.3), we further exactly obtain that:…”

Dynamic power optimization for secondary wearable biosensors in e-healthcare leveraging cognitive WBSNs with imperfect spectrum sensing

“…In this case, the interference power constraint should be imposed to protect PMDs from unavoidable electromagnetic interference caused by all SWBs. For PMD n, the accumulated interference caused by the current active SWB set working over channel k at time t must be kept below the interference temperature limit I max n,k given as follows [31]:…”