AI

Autonomous spectrum balancing for digital subscriber lines

Abstract

The main performance bottleneck of modern digital subscriber line (DSL) networks is the crosstalk among different lines (i.e., users). By deploying dynamic spectrum management (DSM) techniques and reducing excess crosstalk among users, a network operator can dramatically increase the data rates and service reach of broadband access. However, current DSM algorithms suffer from either substantial suboptimality in typical deployment scenarios or prohibitively high complexity due to centralized computation. This paper develops, analyzes, and simulates a new suite of DSM algorithms for DSL interference-channel models called autonomous spectrum balancing (ASB). The ASB algorithms utilize the concept of a "reference line," which mimics a typical victim line in the interference channel. In ASB, each modem tries to minimize the harm it causes to the reference line under the constraint of achieving its own target data-rate. Since the reference line is based on the statistics of the entire network, rather than any specific knowledge of the binder a modem operates in, ASB can be implemented autonomously without the need for a centralized spectrum management center. ASB has a low complexity and simulations using a realistic simulator show that it achieves large performance gains over existing autonomous algorithms, coming close to the optimal rate region in some typical scenarios. Sufficient conditions for convergence of ASB are also proved.