60,142 research outputs found
A Fast Constructive Algorithm For Fixed Channel Assignment Problem
With limited frequency spectrum and an increasing demand for mobile communication services, the problem of channel assignment becoems increasingly important. It has been shown that this problem is equivalent to the graph coloring problem, which is an NP-hard problem [1]. In this work, a fast constructive algorithm is introduced to solve the problem. THe objective of the algorithm is to obtain a conflict free channel assignment to cells which satisfies traffic demand requirements. THe algorithm was tested on several benchmarks problem, and conflict free results were obtained within one second. More, the quality of solution obtained was always same or better than the other reported techniques
A fast constructive algorithm for fixed channel assignment problem
With limited frequency spectrum and an increasing demand for mobile communication services, the problem of channel assignment becomes increasingly important. It has been shown that this problem is equivalent to the graph-coloring problem, which is an NP-hard problem. In this work, a fast constructive algorithm is introduced to solve the problem. The objective of the algorithm is to obtain a conflict free channel assignment to cells which satisfies traffic demand requirements. The algorithm was tested on several benchmark problems, and conflict free results were obtained a within one second. Moreover, the quality of solution obtained was always same or better than the other reported technique
Feedback Allocation For OFDMA Systems With Slow Frequency-domain Scheduling
We study the problem of allocating limited feedback resources across multiple
users in an orthogonal-frequency-division-multiple-access downlink system with
slow frequency-domain scheduling. Many flavors of slow frequency-domain
scheduling (e.g., persistent scheduling, semi-persistent scheduling), that
adapt user-sub-band assignments on a slower time-scale, are being considered in
standards such as 3GPP Long-Term Evolution. In this paper, we develop a
feedback allocation algorithm that operates in conjunction with any arbitrary
slow frequency-domain scheduler with the goal of improving the throughput of
the system. Given a user-sub-band assignment chosen by the scheduler, the
feedback allocation algorithm involves solving a weighted sum-rate maximization
at each (slow) scheduling instant. We first develop an optimal
dynamic-programming-based algorithm to solve the feedback allocation problem
with pseudo-polynomial complexity in the number of users and in the total
feedback bit budget. We then propose two approximation algorithms with
complexity further reduced, for scenarios where the problem exhibits additional
structure.Comment: Accepted to IEEE Transactions on Signal Processin
Resource Allocation for Downlink Multi-Cell OFDMA Cognitive Radio Network Using Hungarian Method
This paper considers the problem of resource allocation for downlink part of an OFDM-based multi-cell cognitive radio network which consists of multiple secondary transmitters and receivers communicating simultaneously in the presence of multiple primary users. We present a new framework to maximize the total data throughput of secondary users by means of subchannel assignment, while ensuring interference leakage to PUs is below a threshold. In this framework, we first formulate the resource allocation problem as a nonlinear and non-convex optimization problem. Then we represent the problem as a maximum weighted matching in a bipartite graph and propose an iterative algorithm based on Hungarian method to solve it. The present contribution develops an efficient subchannel allocation algorithm that assigns subchannels to the secondary users without the perfect knowledge of fading channel gain between cognitive radio transmitter and primary receivers. The performance of the proposed subcarrier allocation algorithm is compared with a blind subchannel allocation as well as another scheme with the perfect knowledge of channel-state information. Simulation results reveal that a significant performance advantage can still be realized, even if the optimization at the secondary network is based on imperfect network information
An Exact Algorithm for the Generalized List -Coloring Problem
The generalized list -coloring is a common generalization of many graph
coloring models, including classical coloring, -labeling, channel
assignment and -coloring. Every vertex from the input graph has a list of
permitted labels. Moreover, every edge has a set of forbidden differences. We
ask for such a labeling of vertices of the input graph with natural numbers, in
which every vertex gets a label from its list of permitted labels and the
difference of labels of the endpoints of each edge does not belong to the set
of forbidden differences of this edge. In this paper we present an exact
algorithm solving this problem, running in time ,
where is the maximum forbidden difference over all edges of the input
graph and is the number of its vertices. Moreover, we show how to improve
this bound if the input graph has some special structure, e.g. a bounded
maximum degree, no big induced stars or a perfect matching
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