Abstract
This paper studies the base stations deployment problem in orthogonal frequency-division multiple access (OFDMA) based private wireless access networks for smart grid (SG). Firstly, we analyze the differences between private wireless access networks for SG and public cellular access networks. Then, we propose scheduling and power control based algorithms for the radio resource allocation subproblem and K-means, simulated annealing (SA), and particle swarm optimization (PSO) based algorithms for the base station (BS) location selection subproblem and iterate over these two sets of algorithms to solve the target problem. Simulation results show that the proposed method can effectively solve the target problem. Specifically, the combination of power control based resource allocation algorithm and PSO based location selection algorithm is recommended.
Highlights
It is critical that the underlying communication technology shall support efficient data exchange between various domains comprising smart grid (SG) [1]
The velocities of the particles are updated according to km[t+1] = wkm[t] + c1ξ(P[mt] − Z[mt]) + c2η(P∗[t] − Z[mt]), where P[mt] is the position of the best solution found by the mth particle at iteration t, P∗[t] is the position of the best solution found by all particles during the search so far, and ξ and η are random values generated by the uniform distribution in the interval [0, 1]
We can find that the payoff of the best location selection is increased gradually and will be converged to a constant value
Summary
It is critical that the underlying communication technology shall support efficient data exchange between various domains comprising smart grid (SG) [1]. In addition to the separation for the wireline segment, data transmission over the wireless segment of data path shall be separated for different types of devices. As a first step towards addressing the above issues, this paper investigates the problem of how to deploy BSs and allocate wireless resources so that the uplink transmission requirements are efficiently met. For this problem, we propose to decompose it into the resource allocation subproblem and the location selection subproblem and solve these two subproblems in an iterative fashion.
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