Resource Allocation Algorithms Supporting Coexistence of Cognitive Vehicular and IEEE 802.22 Networks

Many studies show that the dedicated short range communication (DSRC) band is insufficient to carry increasing wireless traffic demands in vehicular networks. The release of TV white space band by the Federal Communications Commission (FCC) for cognitive access provides additional bandwidth to solve...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 16; no. 2; pp. 1066 - 1079
Main Authors: You Han, Ekici, Eylem, Kremo, Haris, Altintas, Onur
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
channel allocation
Cognitive vehicular networks
FCC
IEEE 802.22 Standard
Integer programming
IP (Internet Protocol)
linear programming
Mixed integer
Networks
Nonlinear programming
Portable equipment
Power control
Resource allocation
Resource management
Rounding
submodular set function
Vehicles
Wireless communication
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:Many studies show that the dedicated short range communication (DSRC) band is insufficient to carry increasing wireless traffic demands in vehicular networks. The release of TV white space band by the Federal Communications Commission (FCC) for cognitive access provides additional bandwidth to solve the DSRC spectrum scarcity problem. However, FCC requires portable devices to use significantly lower transmitting power than fixed devices, which creates a challenging coexistence environment for portable (e.g., vehicular) and fixed (e.g., IEEE 802.22) networks. In this paper, we address the coexistence problem between a vehicular and an 802.22 network via resource allocation. We first formulate the coexistence problem as a mixed-integer nonlinear programming (MINLP) problem, to which three algorithms are developed. The first algorithm converts the MINLP into a convex program and obtains a near-optimal solution to the initial MINLP. In the other two algorithms, we first convert the MINLP into an integer programming (IP) problem. Then, we solve the linear program relaxation of the IP and obtain a fractional solution. Thereafter, two rounding algorithms are developed to round the fractional solution based on column-sparse packing and dependent rounding techniques, respectively. Finally, we compare the performance of the proposed algorithms with an optimal MINLP solver through numerical examples.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2016.2636280