In recent years, there has been a growing interest in multiple access communication systems that spread their transmitted energy over very large bandwidths. These systems, which are referred to as ultra wide-band (UWB) systems, have various advantages over narrow-band and conventional wide-band systems. The importance of multiuser detection for achieving high data or low bit error rates in these systems has already been established in several studies. This paper presents iterative ("turbo") multiuser detection for impulse radio (IR) UWB systems over multipath channels. While this approach is demonstrated for UWB signals, it can also be used in other systems that use similar types of signaling. When applied to the type of signals used by UWB systems, the complexity of the proposed detector can be quite low. Also, two very low complexity implementations of the iterative multiuser detection scheme are proposed based on Gaussian approximation and soft interference cancellation. The performance of these detectors is assessed using simulations that demonstrate their favorable properties.

Impulse radio (IR) systems have drawn attention during the last few years. These systems are planned to coexist with narrowband systems without interfering them. Nevertheless, the narrowband systems can cause interference which may jam the IR receiver. This letter analyzes a low-complexity narrowband interference (NBI)-mitigation algorithm for IR systems, based on minimal mean-square error combining. Theoretical analysis reveals that these algorithms nearly eliminate the NBI. The concept is also extended to the case where the receiver has more correlators than channel taps.

One of the features characterizing almost every multiple-access (MA) communication system is the processing gain. Through the use of spreading sequences, the processing gain of random code-division multiple-access (RCDMA) systems, or any other code-division multiple-access (CDMA) systems, is devoted to both bandwidth expansion and orthogonalization of the signals transmitted by different users. Another type of MA system is impulse radio (IR). IR systems promise to deliver high data rates over ultra-wideband channels with low-complexity transmitters and receivers. In many aspects, IR systems are similar to time-division MA systems, and the processing gain of IR systems represents the ratio between the actual transmission time and the total time between two consecutive transmissions (on-plus-off-to-on ratio). While CDMA systems, which constantly excite the channel, rely on spreading sequences to orthogonalize the signals transmitted by different users, IR systems transmit a series of short pulses, and the orthogonalization between the signals transmitted by different users is achieved by the fact that most of the pulses do not collide with each other at the receiver. In this paper, a general class of MA communication systems that use both types of processing gain is presented, and both IR and RCDMA systems are demonstrated to be two special cases of this more general class of systems. The bit-error rate of several receivers as a function of the ratio between the two types of processing gain is analyzed and compared, under the constraint that the total processing gain of the system is large and fixed. It is demonstrated that in non-intersymbol interference (ISI) channels, there is no tradeoff between the two types of processing gain. However, in ISI channels, a tradeoff between the two types of processing gain exists. In addition, the suboptimality of RCDMA systems in frequency-selective channels is established.

Detecting the number of sources is a well known and a well investigated problem. In this problem, the number of sources impinging on an array of sensors is to be estimated. The widely used processor is based on the Minimum Description Length (MDL) criterion where the array is assumed unstructured and the radiating sources are assumed Gaussian. This paper presents an asymptotic analysis of the performance of a different estimator, when some a-priori information is utilized: In particular, the performance of the MDL estimator which assumes Gaussian sources and a structured array when applied to Gaussian sources is analyzed. It is shown that appropriate use of the prior knowledge about the array geometry can lead to significant improvement in the detection performance. Simulation results show good fit between the empirical and the theoretical results.

In many applications there exists an array of cells (or bins), each containing either an activity (signal) plus noise, or noise only. A common problem is to identify the active bins, assuming that the noise level in the array is unknown. In this paper we present a novel approach for solving this problem. The approach is based on two steps. In the first, we estimate the noise level and in the second we perform a sequential test to decide, for each bin, whether it is active or not. We show that the proposed algorithm collapses to well known special cases. The performance of the proposed algorithm is analyzed analytically and is demonstrated via simulation results.

A new approach for estimating the number of radiating, not fully correlated sources using the data received by an array of sensors is presented. The common approach is to apply information theoretic criteria, such as the minimum description length (MDL) or the Akaike information criterion (AIC), on the received data. Alternatively, we suggest to apply these criteria on the ordered eigenvalues of the sample data covariance matrix. While asymptotically, as the number of snapshots tends to infinity, the two approaches converge, we demonstrate that for any finite number of samples there exist physical conditions for which the proposed approach outperforms the traditional one. These cases are associated with spatially close sources, or with highly correlated sources, or with the case of sources with very different signal-to-noise ratio (SNR).

The overall number of nearest neighbors in bounded distance decoding (BDD) algorithms is given by N/sub 0,eff/=N/sub 0/+N/sub BDD/. Where NBDD denotes the number of additional, non-codeword, neighbors that are generated during the (suboptimal) decoding process. We identify and enumerate the nearest neighbors associated with the original generalized minimum distance (GMD) and Chase (1972) decoding algorithms. After careful examination of the decision regions of these algorithms, we derive an approximated probability ratio between the error contribution of a noncodeword neighbor (one of N/sub BDD/ points) and a codeword nearest neighbor. For Chase algorithm 1 it is shown that the contribution to the error probability of a noncodeword nearest neighbor is a factor of 2/sup d-1/ less than the contribution of a codeword, while for Chase algorithm 2 the factor is 2/sup [d/2]-1/, d being the minimum Hamming distance of the code. For Chase algorithm 3 and GMD, a recursive procedure for calculating this ratio, which turns out to be nonexponential in d, is presented. This procedure can also be used for specifically identifying the error patterns associated with Chase algorithm 3 and GMD. Utilizing the probability ratio, we propose an improved approximated upper bound on the probability of error based on the union bound approach. Simulation results are given to demonstrate and support the analytical derivations.