Zhi Tian

Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks

UWB technology provides an excellent means for wireless positioning due to its high resolution capability in the time domain. Its ability to resolve multipath components makes it possible to obtain accurate location estimates without the need for complex estimation algorithms. In this article, theoretical limits for TOA estimation and TOA-based location estimation for UWB systems have been considered. Due to the complexity of the optimal schemes, suboptimal but practical alternatives have been emphasized. Performance limits for hybrid TOA/SS and TDOA/SS schemes have also been considered. Although the fundamental mechanisms for localization, including AOA-, TOA-, TDOA-, and SS-based methods, apply to all radio air interface, some positioning techniques are favored by UWB-based systems using ultrawide bandwidths.

Compressed Sensing for Wideband Cognitive Radios

In the emerging paradigm of open spectrum access, cognitive radios dynamically sense the radio-spectrum environment and must rapidly tune their transmitter parameters to efficiently utilize the available spectrum. The unprecedented radio agility envisioned, calls for fast and accurate spectrum sensing over a wide bandwidth, which challenges traditional spectral estimation methods typically operating at or above Nyquist rates. Capitalizing on the sparseness of the signal spectrum in open-access networks, this paper develops compressed sensing techniques tailored for the coarse sensing task of spectrum hole identification. Sub-Nyquist rate samples are utilized to detect and classify frequency bands via a wavelet-based edge detector. Because spectrum location estimation takes priority over fine-scale signal reconstruction, the proposed novel sensing algorithms are robust to noise and can afford reduced sampling rates.

A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios

In cognitive radio networks, the first cognitive task preceding any form of dynamic spectrum management is the sensing and identification of spectrum holes in wireless environments. This paper develops a wavelet approach to efficient spectrum sensing of wideband channels. The signal spectrum over a wide frequency band is decomposed into elementary building blocks of subbands that are well characterized by local irregularities in frequency. As a powerful mathematical tool for analyzing singularities and edges, the wavelet transform is employed to detect and estimate the local spectral irregular structure, which carries important information on the frequency locations and power spectral densities of the subbands. Along this line, a couple of wideband spectrum sensing techniques are developed based on the local maxima of the wavelet transform modulus and the multi-scale wavelet products. The proposed sensing techniques provide an effective radio sensing architecture to identify and locate spectrum holes in the signal spectrum

Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks

In wideband cognitive radio (CR) networks, spectrum sensing is an essential task for enabling dynamic spectrum sharing, but entails several major technical challenges: very high sampling rates required for wideband processing, limited power and computing resources per CR, frequency-selective wireless fading, and interference due to signal leakage from other coexisting CRs. In this paper, a cooperative approach to wideband spectrum sensing is developed to overcome these challenges. To effectively reduce the data acquisition costs, a compressive sampling mechanism is utilized which exploits the signal sparsity induced by network spectrum under-utilization. To collect spatial diversity against wireless fading, multiple CRs collaborate during the sensing task by enforcing consensus among local spectral estimates; accordingly, a decentralized consensus optimization algorithm is derived to attain high sensing performance at a reasonable computational cost and power overhead. To identify spurious spectral estimates due to interfering CRs, the orthogonality between the spectrum of primary users and that of CRs is imposed as constraints for consensus optimization during distributed collaborative sensing. These decentralized techniques are developed for both cases of with and without channel knowledge. Simulations testify the effectiveness of the proposed cooperative sensing approach in multi-hop CR networks.

Compressed Wideband Sensing in Cooperative Cognitive Radio Networks

In emerging cognitive radio (CR) networks with spectrum sharing, the first cognitive task preceding any dynamic spectrum access is the sensing and identification of spectral holes in wireless environments. This paper develops a distributed compressed spectrum sensing approach for (ultra-)wideband CR networks. Compressed sensing is performed at local CRs to scan the very wide spectrum at practical signal-acquisition complexity. Meanwhile, spectral estimates from multiple local CR detectors are fused to collect spatial diversity gain, which improves the sensing quality especially under fading channels. New distributed consensus algorithms are developed for collaborative sensing and fusion. Using only one-hop local communications, these distributed algorithms converge fast to the globally optimal solutions even for multi-hop CR networks, at low communication and computation load scalable to the network size.

Cyclic Feature Detection With Sub-Nyquist Sampling for Wideband Spectrum Sensing

For cognitive radio networks, efficient and robust spectrum sensing is a crucial enabling step for dynamic spectrum access. Cognitive radios need to not only rapidly identify spectrum opportunities over very wide bandwidth, but also make reliable decisions in noise-uncertain environments. Cyclic spectrum sensing techniques work well under noise uncertainty, but require high-rate sampling which is very costly in the wideband regime. This paper develops robust and compressive wideband spectrum sensing techniques by exploiting the unique sparsity property of the two-dimensional cyclic spectra of communications signals. To do so, a new compressed sensing framework is proposed for extracting useful second-order statistics of wideband random signals from digital samples taken at sub-Nyquist rates. The time-varying cross-correlation functions of these compressive samples are formulated to reveal the cyclic spectrum, which is then used to simultaneously detect multiple signal sources over the entire wide band. Because the proposed wideband cyclic spectrum estimator utilizes all the cross-correlation terms of compressive samples to extract second-order statistics, it is also able to recover the power spectra of stationary signals as a special case, permitting lossless rate compression even for non-sparse signals. Simulation results demonstrate the robustness of the proposed spectrum sensing algorithms against both sampling rate reduction and noise uncertainty in wireless networks.

A recursive least squares implementation for LCMP beamforming under quadratic constraint

Quadratic constraints on the weight vector of an adaptive linearly constrained minimum power (LCMP) beamformer can improve robustness to pointing errors and to random perturbations in sensor parameters. We propose a technique for implementing a quadratic inequality constraint with recursive least squares (RLS) updating. A variable diagonal loading term is added at each step, where the amount of loading has a closed-form solution. Simulations under different scenarios demonstrate that this algorithm has better interference suppression than both the RLS beamformer with no quadratic constraint and the RLS beamformer using the scaled projection technique, as well as faster convergence than LMS beamformers.

A GLRT approach to data-aided timing acquisition in UWB radios-Part I: algorithms

Realizing the great potential of impulse radio communications depends critically on the success of timing acquisition. To this end, optimum data-aided (DA) timing offset estimators are derived in this paper based on the maximum likelihood (ML) criterion. Specifically, generalized likelihood ratio tests (GLRTs) are employed to detect an ultrawideband (UWB) waveform propagating through dense multipath and to estimate the associated timing and channel parameters in closed form. Capitalizing on the pulse repetition pattern, the GLRT boils down to an amplitude estimation problem, based on which closed-form timing acquisition estimates can be obtained without invoking any line search. The proposed algorithms only employ digital samples collected at a low symbol rate, thus reducing considerably the implementation complexity and acquisition time. Analytical acquisition performance bounds and corroborating simulations are also provided.

Decentralized Sparse Signal Recovery for Compressive Sleeping Wireless Sensor Networks

This paper develops an optimal decentralized algorithm for sparse signal recovery and demonstrates its application in monitoring localized phenomena using energy-constrained large-scale wireless sensor networks. Capitalizing on the spatial sparsity of localized phenomena, compressive data collection is enforced by turning off a fraction of sensors using a simple random node sleeping strategy, which conserves sensing energy and prolongs network lifetime. In the absence of a fusion center, sparse signal recovery via decentralized in-network processing is developed, based on a consensus optimization formulation and the alternating direction method of multipliers. In the proposed algorithm, each active sensor monitors and recovers its local region only, collaborates with its neighboring active sensors through low-power one-hop communication, and iteratively improves the local estimates until reaching the global optimum. Because each sensor monitors the local region rather than the entire large field, the iterative algorithm converges fast, in addition to being scalable in terms of transmission and computation costs. Further, through collaboration, the sensing performance is globally optimal and attains a high spatial resolution commensurate with the node density of the original network containing both active and inactive sensors. Simulations demonstrate the performance of the proposed approach.

Near-optimum soft decision equalization for frequency selective MIMO channels

In this paper, we develop soft decision equalization (SDE) techniques for frequency selective multiple-input multiple-output (MIMO) channels in the quest for low-complexity equalizers with error performance competitive to that of maximum likelihood (ML) sequence detection. We demonstrate that decision feedback equalization (DFE) based on soft-decisions, expressed via the posterior probabilities associated with feedback symbols, is able to outperform hard-decision DFE, with a low computational cost that is polynomial in the number of symbols to be recovered and linear in the signal constellation size. Building on the probabilistic data association (PDA) multiuser detector, we present two new MIMO equalization solutions to handle the distinctive channel memory. The first SDE algorithm adopts a zero-padded transmission structure to convert the challenging sequence detection problem into a block-by-block least-square formulation. It introduces key enhancement to the original PDA to enable applications in rank-deficient channels and for higher level modulations. The second SDE algorithm takes advantage of the Toeplitz channel matrix structure embodied in an equalization problem. It processes the data samples through a series of overlapping sliding windows to reduce complexity and, at the same time, performs implicit noise tracking to maintain near-optimum performance. With their low complexity, simple implementations, and impressive near-optimum performance offered by iterative soft-decision processing, the proposed SDE methods are attractive candidates to deliver efficient reception solutions to practical high-capacity MIMO systems. Simulation comparisons of our SDE methods with minimum-mean-square error (MMSE)-based MIMO DFE and sphere decoded quasi-ML detection are presented.