Input-Output Clustering Criterion (IOCC) for Optimizing Distributed Antenna Locations
Zekeriya Uykan, Riku Jantti

TL;DR
This paper introduces the Input-Output Clustering Criterion (IOCC), a novel method for optimizing distributed antenna locations by minimizing capacity error through a clustering approach in input-output space, outperforming previous methods.
Contribution
The paper proposes IOCC, a new clustering-based criterion that optimizes antenna placement by considering both location and capacity, extending existing input-only methods.
Findings
IOCC provides an upper bound to capacity error.
The upper bound equals a weighted quantization error in input-output space.
Increasing RAUs reduces the capacity error bound, improving system performance.
Abstract
In this paper, we propose an input-output space clustering criterion (IOCC) to optimize the locations of the remote antenna units (RAUs) of generalized Distributed Antenna Systems (DASs) under sum power constraint. In IOCC, the input space refers to RAU location space and output space refers to location specific ergodic capacity space for noise-limited environments. Given a location-specific arbitrary desired ergodic capacity function over a geographical area, we define the error as the difference between actual and desired ergodic capacity. Our investigations show that i) the IOCC provides an upper bound to the cell averaged ergodic capacity error; and ii) the derived upper bound is equal to a weighted quantization error function in location-capacity space (input-output space) and iii) the upper bound can be made arbitrarily small by a clustering process increasing the number of RAUs…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Wireless Communication Networks Research · Advanced Wireless Network Optimization
