Wireless Communication Systems

Adaptive Receiver Structures We next consider adaptive algorithms for sequentially estimating the blind linear detector. First, we address adaptive implementation of the blind channel estimator discussed above. Suppose that the signal subspace Us is known. Denote by z[i] the projection of ... [full story]

NAHJ Subspace Tracking The algorithm we present here was developed in [411, 412]. It is a member of the QR-Jacobi family in the sense that it uses Givens rotations during the updating process. However, this algorithm avoids the QR step entirely. ... [full story]

PASTd Algorithm Let be a random vector with autocorrelation matrix Cr = E{r[i]r[i]H}. Consider the scalar function Equation 2.150 with a matrix argument (r < N). It can be shown that [586]: W is a stationary point of ... [full story]

Subspace Tracking Algorithms It is seen from Section 2.5 that the linear multiuser detectors are obtained once the signal subspace components are identified. The classic approach to subspace estimation is through batch eigenvalue decomposition (ED) of the sample autocorrelation matrix or ... [full story]

Performance of Blind Multiuser Detectors 2.5.1 Performance Measures In previous sections we have discussed two approaches to blind multiuser detection: the direct method and the subspace method. These two approaches are based primarily on two equivalent expressions for the linear MMSE detector ... [full story]

Asymptotics of Detector Estimates We next examine the consistency and asymptotic variance of the estimates of the two subspace linear detectors. Assuming that the received signal samples are independent and identically distributed (i.i.d.), then by the strong law of large numbers, ... [full story]

Linear Decorrelating Detector The linear decorrelating detector given by (2.13) is characterized by the following results. Lemma 2.1: The linear decorrelating detector d1 in (2.13) is the unique weight vector w range(Us), such that wHs1 = 1, and wHsk = 0 ... [full story]

QR-RLS Algorithm The RLS approach discussed in Section 2.3.2, which is based on the matrix inversion lemma for recursively updating Cr[i]-1, has complexity per update. Note that although fast RLS algorithms of complexity exist [66, 83, 116, 124], all ... [full story]

RLS Algorithm The LMS algorithm discussed above has a very low computational complexity, on the order of operations per update. However, its convergence is usually very slow. We next consider the recursive least-squares (RLS) algorithm for adaptive implementation of the ... [full story]

LMS Algorithm We first consider the least-mean-squares (LMS) algorithm for recursive estimation of m1 based on (2.32). Define Equation 2.35 as a projection matrix that projects any signal in onto the orthogonal space of s1. Note that m1 can ... [full story]

Blind Multiuser Detection: Direct Methods It is seen from (2.13) and (2.20) that these two linear detectors are expressed in terms of a linear combination of the signature sequences of all K users. Recall that for the matched-filter receiver, the only ... [full story]

Linear MMSE Detector While the linear decorrelating detector is designed to eliminate the MAI completely at the expense of enhancing the ambient noise, the linear MMSE detector, , is designed to minimize the total effect of the MAI and the ambient ... [full story]

Linear Decorrelating Detector A linear decorrelating detector for user 1, , is such that when correlated with the received signal r[i], it results in zero MAI [i.e., the second term in (2.10) is zero]. In particular, the linear decorrelating detector d1 ... [full story]

Synchronous CDMA Signal Model We start by considering the most basic multiple-access signal model: a baseband K-user time-invariant synchronous additive white Gaussian noise (AWGN) system, employing periodic (short) spreading sequences and operating with a coherent BPSK modulation format. (An approach to ... [full story]

Matched Filter/RAKE Receiver We consider first the particular case of the model of (1.9), in which there is only a single user (i.e., K = 1), the channel impulse g1(·, ·) is known to the receiver, there is no CCI [i.e., ... [full story]

Basic Receiver Signal Processing for Wireless This book is concerned with the design of advanced signal processing methods for wireless receivers, based largely on the models discussed in preceding sections. Before moving to these methods, however, it is of interest to ... [full story]

Wireless Channel From a technical point of view, the greatest distinction between wireless and wireline communications lies in the physical properties of wireless channels. These physical properties can be described in terms of several distinct phenomena, including ambient noise, propagation losses, ... [full story]

Multiple-Access Techniques In Section 1.2.1 we discussed ways in which a symbol stream associated with a single user can be transmitted. Many wireless channels, particularly in emerging systems, operate as multiple-access systems, in which multiple users share the same radio resources. There ... [full story]

Single-User Modulation Techniques To discuss advanced receiver signal processing methods for wireless, it is useful first to specify a general model for the signal received by a wireless receiver. To do so, we can first think of a single transmitter, transmitting ... [full story]