Low Delay MAC Scheduling for Frequency-Agile Multi-Radio Wireless Networks

Abstract

Abstract-Recent trends suggest that cognitive radio based wireless networks will be frequency agile and the nodes will be equipped with multiple radios capable of tuning across large swaths of spectrum. The MAC scheduling problem in such networks refers to making intelligent decisions on which communication links to activate at which time instant and over which frequency band. The challenge in designing a low-complexity distributed MAC, that achieves low delay, is posed by two additional dimensions of cognitive radio networks: interference graphs and data rates that are frequency-band dependent, and explosion in number of feasible schedules due to large number of available frequency-bands. In this paper, we propose MAXIMAL-GAIN MAC, a distributed MAC scheduler for frequency agile multi-band networks that simultaneously achieves the following: (i) optimal network-delay scaling with respect to the number of communicating pairs, (ii) low computational complexity of O(log 2 (maximum degree of the interference graphs)) which is independent of the number of frequency bands, number of radios per node, and overall size of the network, and (iii) robustness, i.e., it can be adapted to a scenario where nodes are not synchronized and control packets could be lost. Our proposed MAC also achieves a throughput provably within a constant fraction (under isotropic propagation) of the maximum throughput. Due to a recent impossibility result, optimal delay-scaling could only be achieved with some amount of throughput loss [30]. Extensive simulations using OMNeT++ network simulator shows that, compared to a multi-band extension of a state-of-art CSMA algorithm (namely, Q-CSMA), our asynchronous algorithm achieves a 2.5× reduction in delay while achieving at least 85% of the maximum achievable throughput. Our MAC algorithms are derived from a novel local search based technique.

WhiteFi Infostation: Engineering Vehicular Media Streaming With Geolocation Database

“…We investigate the potentially deployed WhiteFi Infostations for vehicular media streaming by utilizing the TVWS channels in urban city scenario. To make a full use of the available local TVWS spectrum resource, we consider a set of N WhiteFi Infostations denoted by N = {1, 2, ..., N }, which can operate on most M TVWS channels simultaneously with multi-radio configuration [27]. We assume the locations of N WhiteFi Infostations are fixed which are distributed in a D×D squared region.…”

Dynamic spectrum access for energy‐constrained CR: single channel versus switched multichannel

“…1, the SU switches to the ith channel at n 1 . Pgf of τ, the SU remains on the channel before switching to the next is given by (7). Elapsed time between successive ith channel access instances is given as σ. Pdf and pgf of σ is given as H (n) and H (z).…”

“…To avoid channel access conflict, a PU channel is allocated to one user at a time. A SU could be allocated a dedicated single channel [3][4][5] or a set of dedicated multiple channels [6][7][8] for their operation.…”

Towards exploiting a synergy between cognitive and multi-radio networking

“…However, this augmentation also demands modified routing, mediumaccess, and link-layer protocols [21], [22]. Nonetheless, as the primary motive of deploying cognitive radio networks is to improve the performance of secondary users through spectrum utilization and deploying multiple radios in wireless nodes is known to improve the performance of a user, it is intuitive that simultaneous utilization of both these techniques, i.e., multiradio cognitive networks, will result in significantly improved network performance.…”