Project Title: Diversity Modeling for Wireless Communications

1. Wireless and Network Engineering Lehigh University 2003 Wireless Day Open House Project Title: Diversity Modeling for Wireless Communications PI: Shalinee Kishore, Ph.D. Graduate Assistant: Yan Li Introductory Some Areas of Current Research Background and Relevance The random, time-varying channel characteristics between a transmitter and a receiver pose many challenges to reliable wireless communications, among them is variable fading of the received signal. Due to a cumulative effect of reflection, diffraction, or scattering of electromagnetic waves in the physical environment, random fading over the channel can lead to variable performance over the communication link, fluctuating from extremely reliable received data during high channel gains to unrecoverable received data during low channel gains. Diversity methods attempt to counter this disparity, in particular, they focus on overcoming unreliability caused by poor fading conditions. Diversity techniques can be categorized into two classes, a traditional class developed for point-to-point communications and an emerging class focused succinctly on multiuser systems. Although the benefits of these various diversity schemes are well-documented, it is not clear how to determine the amount of diversity that is available for a given channel and system description. Typically, channel diversity is quantified using indirect means, for instance, the slope of the bit error rate (BER) curves. Objectives To answer these needs, this research project will use a quantity called the diversity factor to measure system diversity. Simple and reliable analytical methods will be developed to compute the diversity factor for various channel and system conditions. This diversity factor will then be used to compute performance gains offered by various point-to-point and multiuser diversity schemes. The diversity models developed here will also be used to study some fundamental design problems in wireless communication systems. • Point to Point Methods • Our research is presently focused on quantifying the performance of various point-to-point diversity schemes using the diversity factor. For example, • Multipath diversity in CDMA systems: In CDMA systems, RAKE receivers collect and combine time-delayed versions of the transmitted signal that arrive along various multiple paths between transmit and receive antennas. (See Fig. 1) • Multiple Antenna Diversity: Multiple antenna systems, which are usually modeled as Multiple-Input Multiple-Output (MIMO) systems, can offer significant gains in terms of diversity as well as spatial multiplexing. • Multiuser Methods • We also study diversity factors for multiuser systems. Recent results demonstrate in data networks overall throughput can be improved by assigning radio resources to the sole user with the highest channel gain. The underlying assumption of time-varying fading implies that over a large enough time window, this exclusive access will be granted to all users and overall throughput will be improved. Modifications to this multiuser scheduling method that additionally account for delay constraints and/or fairness have also been proposed. • We have developed preliminary results to verify that the diversity factor can be used to quantify multiuser diversity gains when resources are allocated to the user with the highest path gain (See Fig. 2). We will develop analytical support for these results and examine its impact on scheduling algorithms that consider delay constraints and fairness. Fig. 1: Multipath Diversity Effects on CDMA Uplink User Capacity Diversity Techniques Point-to-Point Methods Time, frequency, space techniques that collect and combine multiple, independently-fading received copies of transmitted signal. Multiuser Methods Scheduling algorithms that assign radio resources (power, bandwidth, antenna, etc.) to users according to fading conditions. Total Number of Users, N Diversity Factor, DF BER Not immediately clear what are benefits in terms of network performance, e.g., user capacity, data rates, etc. SNR Fig. 2: Capacity in Multiuser Environment for Various Average SNR’s Diversity Factor and the Uniform Environment Capacity, C = log (1+SNR hmax) Assume in a general diversity scheme, there are L received signals. In the case of point-to-point systems, the L received signals are copies of the same transmitted signal, where each path experiences independent fading. In multiuser systems, we can assume the L received signals originate from different users. The Diversity Factor, DF, is defined as: where rk is the channel gain (fading coefficient) for received signal k. This diversity factor can be computed for any diversity scheme and is measure of the actual diversity available to the system in the given channel conditions. If we assume the fading characteristics of the environment lead to independent, Rayleigh-fading paths such that each of the L received signals have the same mean power, then the diversity system is operating over a uniform environment. In this case, it can be shown that DF = L. As opposed to non-uniform environments where mean power varies among the L received signals, the uniform environment lends its itself to simpler analysis. Our results show that channels with the same diversity factor lead to roughly the same performance. Diversity Factor, DF Future Areas of Research We believe that the diversity factor will be helpful in tackling important design problems in wireless communications. Specifically, we aim to study its impact onchannel estimation and radio resource allocation. Accurate channel estimates are needed in various techniques, including MIMO and multiuser diversity scheduling. We believe rough estimates of an environment’s diversity factor can be used in conjunction with robust signal processing methods to improve channel estimates. Measurements of diversity factors may also be helpful in specifying radio resource allocations that address delay constraints and fairness requirements in multiuser systems.