@conference {566,
	title = {Efficient SER Estimation for MIMO Detectors via Importance Sampling Schemes},
	booktitle = {Asilomar Conference on Signals, Systems, and Computer},
	year = {2019},
	month = {November},
	pages = {712-716},
	address = {Pacific Grove, CA, USA},
	abstract = {In this paper we propose two importance sampling methods for the efficient symbol error rate (SER) estimation of maximum likelihood (ML) multiple-input multiple-output (MIMO) detectors. Conditioned to a given transmitted symbol, computing the SER requires the evaluation of an integral outside a given polytope in a high-dimensional space, for which a closed-form solution does not exist. Therefore, Monte Carlo (MC) simulation is typically used to estimate the SER, although a naive or raw MC implementation can be very inefficient at high signal-to-noise-ratios or for systems with stringent SER requirements. A reduced variance estimator is provided by the Truncated Hypersphere Importance Sampling (THIS) method, which samples from a proposal density that excludes the largest hypersphere circumscribed within the Voronoi region of the transmitted vector. A much more efficient estimator is provided by the existing ALOE (which stands for {\textquoteleft}{\textquoteleft}At Least One rare Event{\textquoteright}{\textquoteright}) method, which samples conditionally on an error taking place. The paper describes in detail these two IS methods, discussing their advantages and limitations, and comparing their performances.},
	author = {Elvira, Victor and Santamar{\'\i}a, Ignacio}
}
