A. Lee Swindlehurst

Wireless indoor channel modeling: statistical agreement of ray tracing simulations and channel sounding measurements

A statistical space-time model for indoor wireless propagation based on empirical measurements is compared with results from the deterministic ray-tracing simulation tool WiSE for the same environment. Excellent agreement is found in terms of the distributions of arrival times and angular spread for both modeling approaches. The WiSE package is also use to synthesize MIMO channel matrices and determine the theoretical capacity, available in the tested environments. It is found that, for narrowband channels, the spatial clustering of the multipaths limits the capacity gains associated with increased array size.

A hybrid approach to spatial multiplexing in multiuser MIMO downlinks

In the downlink of a multiuser multiple-input multiple-output (MIMO) communication system, simultaneous transmission to several users requires joint optimization of the transmitted signals. Allowing all users to have multiple antennas adds an additional degree of complexity to the problem. In this paper, we examine the case where a single base station transmits to multiple users using linear processing (beamforming) at each of the antenna arrays. We propose generalizations of several previous iterative algorithms for multiuser transmit beamforming that allow multiple antennas and multiple data streams for each user, and that take into account imperfect channel estimates at the transmitter. We then present a new hybrid algorithm that is based on coordinated transmit-receive beamforming, and combines the strengths of nonorthogonal iterative solutions with zero-forcing solutions. The problem of distributing power among the subchannels is solved by using standard bit-loading algorithms combined with the subchannel gains resulting from the zero-forcing solution. The result is a significant performance improvement over equal power distribution. At the same time, the number of iterations required to compute the final solution is reduced.

Performance of MIMO spatial multiplexing algorithms using indoor channel measurements and models

Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602, U.S.A.SummarySeveral algorithms have recently been proposed for multiplexing multiple users in multiple input multiple output(MIMO) downlinks. The ability of a transmitter to accomplish this using spatial methods is generally dependent onwhether the users’ channels are correlated. Up to this point, most of the multiplexing algorithms have been tested onuncorrelated Gaussian channels, a best-case scenario. In this paper, we examine the performance of multiplexingmultiple users under more realistic channel conditions by using indoor channel measurements and a statisticalmodel for test cases. We use a block zero-forcing algorithm to test performance at various user separation distances,optimizing for both maximum throughput under a power constraint and minimum power under a rate constraint.The results show that for the measured indoor environment (rich scattering, non-line-of-sight), a separation of fivewavelengths is enough to achieve close to the maximum available performance for two users. Since many spatialmultiplexing algorithmsrequirechannel state information(CSI) at the transmitter, we alsoexamine the performanceloss due to CSI error. The results show that a user can move up to one-half wavelength before the original channelmeasurement becomes unusable. Copyright # 2004 John Wiley & Sons, Ltd.KEY WORDS: MIMO systems; multi-user downlink beamforming; multi-user MIMO systems; MIMO channelmodels; MIMO channel measurement; indoor channel models; indoor channel measurement1. IntroductionMultiple input multiple output (MIMO) wireless sys-tems offer the possibility of spatially multiplexingmultiple users, creating an additional dimension formultiple access beyond time-, frequency- and code-division approaches. The uplink of a multi-userMIMO system follows a model for which numerousalgorithms already exist; although non-trivial, thisproblem has been studied (either directly or in-directly) by numerous researchers. Less understoodis the MIMO downlink or ‘multicast’ channel, where amultiple antenna transmitter (e.g. a basestation) at-tempts to send different messages to multiple users,each of which may be equipped with a multipleantenna receiver. If one assumes that all users haveonly one antenna, the problem is somewhat simplified,and optimal transmitter beamforming solutions exist[1,2], as well as sub-optimal, but simpler solutions [3–5]. When the users have multiple antennas, achieving

A Performance Bound for Interpolation of MIMO-OFDM Channels

The performance of MIMO-OFDM communication systems depends upon accurate channel estimation across frequency bands much wider than the coherence bandwidth of the channel. Obtaining channel estimates at all necessary frequencies typically involves interpolation of channel estimates obtained using pilots at a few distinct frequencies. This paper derives a performance bound for channel estimates obtained via frequency interpolation for slowly-fading frequency-selective channels. Numerical simulations of the bound indicate that normalized interpolation performance improves significantly as the number of antennas increases, suggesting that fewer pilots are needed as antennas are added to a MIMO-OFDM system.

Connectivity in a UAV Multi-static Radar Network

This paper describes a multi-static radar network composed of multiple unmanned air vehicles (UAVs). Time-delay and Doppler measurements taken by the UAV team are passed to a centralized processor to determine optimal heading commands for the UAV team to reduce tracking error. However, due to power and communication constraints on the UAVs, the optimal heading could cause individual UAVs or perhaps the entire network to become disconnected from the base station. In this paper, we ensure that the UAV team remains connected to the base by adding a connectivity constraint to the optimization criterion. This constraint is a modification to the “geometric connectivity robustness” metric proposed recently by Spanos and allows for a balance between the sensor network’s performance and connectivity, while ensuring the appropriate connections for necessary information flow.

Blind and semi-blind equalization for generalized space-time precoding

This paper presents a general framework for space-time codes that encompasses a number of recently proposed schemes as special cases. The space-time codes considered are block codes that employ arbitrary redundant linear precoding of a given data sequence together with embedded training symbols, if any. The redundancy introduced by the linear precoding imposes structure on the received data that under certain conditions can be exploited for blind or semi-blind estimation of the transmitted sequence (direct approach) or a linear equalizer that recovers the sequence in a second step (indirect approach). Algorithms based on this observation are developed for the flat-fading case, and then extended to handle frequency-selective fading.

Code-timing synchronization in DS-CDMA systems using space-time diversity

The synchronization of a desired user transmitting a known training sequence in a direct-sequence (DS) asynchronous code-division multiple-access (CDMA) system is addressed. It is assumed that the receiver consists of an arbitrary antenna arrayand works in a near–far, frequency -nonselective, slowlyfading channel. The estimator that we propose is derived byapply ing the maximum likelihood (ML) principle to a signal model in which the contribution of all the interfering components (e.g., multiple-access interference, external interference and noise) is modeled as a Gaussian term with an unknown and arbitraryspace–time correlation matrix. The main contribution of this paper is the fact that the estimator makes e7cient use of the structure of the signals in both the space and time domains. Its performance is compared with the Cram+ er–Rao Bound, and with the performance of other methods proposed recentlythat also employan antenna arraybut onlyexploit the structure of the signals in one of the two domains, while using the other simplyas a means of path diversity . It is shown that the use of the temporal and spatial structures is necessary to achieve synchronization in heavily loaded systems or in the presence of directional external interference. ? 2001 Elsevier Science B.V. All rights reserved.

Decision-Directed Multivariate Empirical Bayes Classification with Nonstationary Priors

A decision-directed learning strategy is presented to recursively estimate (i.e., track) the time-varying a priori distribution for a multivariate empirical Bayes adaptive classification rule. The problem is formulated by modeling the prior distribution as a finite-state vector Markov chain and using past decisions to estimate the time evolution of the state of this chain. The solution is obtained by implementing an exact recursive nonlinear estimator for the rate vector of a multivariate discrete-time point process representing the decisions. This estimator obtains the Doob decomposition of the decision process with respect to the a-field generated by all past decisions and corresponds to the nonlinear least squares estimate of the prior distribution. Monte Carlo simulation results are provided to assess the performance of the estimator.

On the identifiability of multipath parameters

Consider a received signal consisting of noise and an unknown waveform that arrives at a sensor through both a direct path and an attenuated and delayed specular path. We show that a commonly used frequency-domain model for such a signal is valid essentially only under the assumption that the waveform that underwent multipath propagation is band-limited. Furthermore, we derive sufficient conditions for the aforementioned model to be identifiable from second-order statistics (i.e., its parameters can be uniquely determined from the spectral density of the received signal). We also show that in the frequently considered case of a white waveform, neither the attenuation coefficient, nor the waveform power, nor the noise power can be unambiguously determined.

Closed-form blind and semi-blind estimation of linear receivers for space-time coding

This paper exploits a special class of space-time codes in which linearly transformed versions of a given data sequence are transmitted from multiple antennas. Several recently proposed codes, including space-time block codes, are members of this class. The redundancy introduced by the transformations imposes structure on the received data that under certain conditions can be exploited for direct blind (and semi-blind) estimation of a linear zero-forcing receiver that recovers the original data sequence. If the transmitted symbols are constant modulus, the space-time code structure can be exploited by the analytic constant modulus (ACM) algorithm to simplify the separation of multiple co-channel users. If each user employs a different code or only one user is present, the ACM joint diagonalization step can be eliminated even though multiple constant modulus data streams are received.This paper exploits a special class of space-time codes in which linearly transformed versions of a given data sequence are transmitted from multiple antennas. Several recently proposed codes, including space-time block codes, are members of this class. The redundancy introduced by the transformations imposes structure on the received data that under certain conditions can be exploited for direct blind (and semi-blind) estimation of a linear zero-forcing receiver that recovers the original data sequence. If the transmitted symbols are constant modulus, the space-time code structure can be exploited by the analytic constant modulus (ACM) algorithm to simplify the separation of multiple co-channel users. If each user employs a different code or only one user is present, the ACM joint diagonalization step can be eliminated even though multiple constant modulus data streams are received.