It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver. Due to the high cost and power consumption of gigasample mixed-signal devices, mmWave precoding will likely be divided among the analog and digital domains. The large number of antennas and the presence of analog beamforming requires the development of mmWave-specific channel estimation and precoding algorithms. This paper develops an adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel. To enable the efficient operation of this algorithm, a novel hierarchical multi-resolution codebook is designed to construct training beamforming vectors with different beamwidths. For single-path channels, an upper bound on the estimation error probability using the proposed algorithm is derived, and some insights into the efficient allocation of the training power among the adaptive stages of the algorithm are obtained. The adaptive channel estimation algorithm is then extended to the multi-path case relying on the sparse nature of the channel. Using the estimated channel, this paper proposes a new hybrid analog/digital precoding algorithm that overcomes the hardware constraints on the analog-only beamforming, and approaches the performance of digital solutions. Simulation results show that the proposed low-complexity channel estimation algorithm achieves comparable precoding gains compared to exhaustive channel training algorithms. The results illustrate that the proposed channel estimation and precoding algorithms can approach the coverage probability achieved by perfect channel knowledge even in the presence of interference.

Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems

Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of apt array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing.

/pdf/application-of-antenna-arrays-to-mobile-communications-ii-52fds1c81j.pdf

Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations

We have provided a review of some recent results on the emerging technology of MIMO radar with colocated antennas. We have shown that the waveform diversity offered by such a MIMO radar system enables significant superiority over its phased-array counterpart, including much improved parameter identifiability, direct applicability of adaptive techniques for parameter estimation, as well as superior flexibility of transmit beampattern designs. We hope that this overview of our recent results on the MIMO radar, along with the related results obtained by our colleagues, will stimulate the interest deserved by this topic in both academia and government agencies as well as industry.

MIMO Radar with Colocated Antennas

High peak-to-average power ratio of the transmit signal is a major drawback of multicarrier transmission such as OFDM or DMT. This article describes some of the important PAPR reduction techniques for multicarrier transmission including amplitude clipping and filtering, coding, partial transmit sequence, selected mapping, interleaving, tone reservation, tone injection, and active constellation extension. Also, we make some remarks on the criteria for PAPR reduction technique selection and briefly address the problem of PAPR reduction in OFDMA and MIMO-OFDM.

An overview of peak-to-average power ratio reduction techniques for multicarrier transmission

The problem addressed is source localization from time differences of arrival (TDOA). This problem is also referred to as hyperbolic localization and it is non-convex in general. Traditional solutions proposed in the literature have generally poor robustness to errors in the TDOA estimates. More recent methods, which relax the non-convex problem to a convex optimization by applying a semi-definite relaxation (SDR) method, were found to be more robust to TDOA errors than the traditional methods. However, the SDR methods are not optimal in general. In this paper, three convex optimization methods with different computational costs are proposed to improve the hyperbolic localization accuracy. The first method takes an SDR approach to relax the hyperbolic localization to a convex optimization. The second method follows a linearized formulation of the problem and seeks for a biased estimate of improved accuracy. The first two methods perform comparably when the source is inside the convex hull of the sensors. When the source is located outside, the second approach performs better, at the cost of higher computation. A third method is proposed by exploiting the source sparsity. With this, the hyperbolic localization is formulated as an l 1 -regularization problem, where the l 1 -norm is used as source sparsity constraint. Computer simulations show that the l 1 -regularization can offer further improved accuracy, but at the cost of additional computational effort.

Time difference of arrival based source localization within a sparse representation framework

We address the localization of sources with known waveforms in frequency-selective channels Conventional localization by multilateration is an indirect approach that is suboptimal at lower SNR, and breaks down in the presence of multipath Here, we propose a direct localization method (DLM) that exploits the sparsity of the emitters, as well as differences in the properties of the line of sight (LOS) versus multipath components of the signals received at the sensors It is shown that the proposed method has superior performance relative to other known localization techniques and is robust to sensors with blocked LOS

Direct localization of emitters using widely spaced sensors in multipath environments

Waveforms transmitted by the elements of a MIMO radar system may differ in power and bandwidth. This raises the question of optimal resource allocation among the radar elements. Specifically, we are asking, given constrained resources, what is the optimal bandwidth and optimal joint power and bandwidth allocation for best target localization performance. Using the Cramer-Rao Lower Bound (CRLB) as the figure of merit for localization accuracy, the resource allocation optimization problem turns out to be non-convex. We apply a Difference of Convex functions programming approach to develop quasi-optimal algorithms for solving the resource allocation problems. A lower bound is also developed to help assess the quality of our solutions. Numerical examples demonstrate that bandwidth allocation has considerably more impact on performance than power allocation, and that the best performance is obtained with joint power and bandwidth allocation. (6 pages)

Resource allocation in radar networks for non-coherent localization

The requirement to suppress narrowband interferences in CDMA communications stems from the overlay concept, ie, coexistence of different types of signals in the same frequency band The conventional approach to rejecting the narrowband interferences has been to whiten the received signal containing the interference, prior to spread spectrum demodulation In this paper, it is proposed to achieve the interference rejection through spatial processing The main benefit of this approach is its robustness with respect to the interference bandwidth Stepping up from single domain spatial processing to space-time processing provides degrees of freedom for both overlay interference cancellation and diversity combining Two space-time architectures, cascade and joint-domain, are studied and compared to a Rake receiver preceded by a whitening filter Main contributions of the paper are the development of analytical expressions of (1) the efficiency of each method, (2) the pdf‘s of the output SNR in a Rayleigh fading environment, and (3) the error probability associated with each method The analysis therein demonstrates that the joint-domain architecture outperforms the cascade configuration, which in turn is superior to the whitening filter-Rake combination

The Performance of Space-Time Processing for Suppressing NarrowbandInterference in CDMA Communications

This work proposes an augmented variation of conventional space-time adaptive processing (STAP), and explores the application of multi-branch matching pursuit (MBMP) to a multiple-input multiple-output (MIMO) beamformer whose steering vector is created over an array having random, inter-element spacing. By applying compressive sensing (CS), a radar system is able to minimize the undesired effects of an undersampled array while providing adequate clutter suppression and reduced burden on array integration. In this paper, we compare the performance and computational complexity of the MBMP applied to the STAP problem and the STAP beamformer. In addition we propose two methods to reduce the computational complexity of MBMP, a modification to the MBMP algorithm which we refer to as truncated MBMP, and a grid refinement technique. We evaluate our approach and extend this aspect to help in understanding the necessary computations required for practical target detection.

Alexander M. Haimovich

Papers

Time difference of arrival based source localization within a sparse representation framework

Direct localization of emitters using widely spaced sensors in multipath environments

Resource allocation in radar networks for non-coherent localization

The Performance of Space-Time Processing for Suppressing NarrowbandInterference in CDMA Communications

Cost analysis of compressive sensing for MIMO STAP random arrays