Enhanced dynamic radio resource allocation performance using a gradient descent algorithm

Abstract

Mobility of the wireless user creates uncertainty in demand and non-optimum use of radio resources. Current wireless networks are periodically re-configured manually to improve network performance. Dynamic channel assignment (DCA) methods can be used to reconfigure radio channels automatically. But many of these DCA techniques use parochial allocation schemes which consider only the received interference levels and do not consider the impact of the new interference generated by a particular channel assignment on other cells. These DCA algorithms typically allocate channels on an instantaneous need basis so there is little computation time to consider the impact of network wide interference. This paper will propose a new DCA approach which uses time intervals to estimate traffic trends. These traffic trends will be used to estimate radio requirements for each period. All investigated DCA algorithms will consider network wide interference impact and select radio configurations which minimize overall interference. This paper will examine the performance of two different DCA approaches, the gradient descent algorithm (GDA) and cell level radio tuning algorithm (CLRTA) methods. The different DCA algorithms will also be compared to static allocation algorithms. The GDA is shown to have superior performance to that of the CLRTA by approximately 2-4 dB depending on the number of available radio channels. The DCA approach is approximately 3-5 dB better than conventional fixed channel allocation (FCA) scheme.