Combating interference is one of the most outstanding challenges in wireless communication networks, especially for cellular networks with frequency reuse one. This issue becomes even more significant in heterogeneous networks where lower power nodes (such as pico-cell and/or femto-cell) are deployed within a macro-cell’s coverage. In this paper, we investigate interference coordination techniques in different scenarios. For homogeneous networks, we study single-cell multiuser MIMO for the purpose of intra-cell interference coordination and coordinated multi-point (CoMP) transmission for the purpose of inter-cell interference mitigation. For multi-user MIMO systems, we will discuss multi-user MIMO proportional-fair scheduling as well as multi-user precoding schemes. For CoMP transmission, we will focus on CoMP joint transmission and discuss the situations where CoMP could provide performance gains over single-cell operations. For heterogeneous networks, we investigate two specific inter-cell interference coordination techniques: time-domain solutions and power setting schemes. Furthermore, the potential extension of various CoMP schemes to heterogeneous networks will also be discussed.

Different centralized approaches such as least-squares (LS) and particle filtering (PF) algorithms have been developed to localize an acoustic source by using a distributed acoustic vector sensor (AVS) array. However, such algorithms are either not applicable for multiple sources or rely heavily on sensor-processor communication. In this paper, a distributed unscented PF (DUPF) approach is proposed for multiple acoustic source tracking. At each distributed AVS node, the first-order and the second-order statistics of the local state are estimated by using an unscented information filter (UIF) based PF. The UIF is employed to approximate the optimum importance function due to its simplicity, by which the matrix operation is the state information matrix rather than the covariance matrix of the measurement sequence. These local statistics are then fused between neighbor nodes and a consensus filter is applied to achieve a global estimation. In such an architecture, only the state statistics need to be transmitted among the neighbor nodes. Consequently, the communication cost can be reduced. The distributed posterior Cramér–Rao bound is also derived. Simulation results show that the performance of the DUPF tracking approach is similar to that of centralized PF algorithm and significantly better than that of LS algorithms.

This paper considers the problem of tracking multiple acoustic sources in 3-D space using a distributed acoustic vector sensor array. Unlike the existing two-stage localization approach, which estimates the direction of arrival of the source at each sensor first and then triangulate a 3-D position, a particle filtering approach is developed to directly fuse the signals collected from distributed sensors. To enhance the tracking performance and constrain the computational complexity, an information filter is developed to approximate the optimal importance sampling. Since the position state of the source is linear with the velocity state, a Rao-Blackwellization step is employed to marginalize out the velocity component. In addition, the posterior Cramer-Rao bound is developed to provide a lower performance bound for the distributed tracking system. Both the numerical study and simulations show that the proposed tracking approach significantly outperforms the two-stage localization approaches for 3-D position estimation.

This paper presents a distributed particle filtering (PF) approach for wideband acoustic source tracking using an acoustic vector sensor (AVS) network. At each distributed AVS node of the AVS network, the unscented information PF (UIPF) provides local estimates of the source location. A distributed consensus algorithm, based on the first and second order statistics of the localized UIPF estimated states, then obtains the global position estimate for the overall AVS network. Since the UIPF inverts smaller order matrices, its computational complexity is significantly less as compared to its centralized counterpart. The performance of the proposed tracking algorithm is studied under both single source and multiple source tracking scenarios.

This paper presents a method for computing inner products based on the distributed arithmetic principles and thermometer codes. The input is represented in the residue domain using thermometer codes while the output is encoded in the one-hot code format. Compared to the conventional methods of evaluating inner products using binary format, the proposed system provides an elegant way of performing the modular inner products computation due to the absence of the 2n modulo operation encountered in binary based methods. In addition, the modulo adder used in the proposed system can be implemented using simple shifter based circuit utilizing one-hot code format with no carry propagation involved in the addition.

In this paper, a low-complexity algorithm SAGE-USL is presented for 3-dimensional (3-D) localization of multiple acoustic sources in a shallow ocean with non-Gaussian ambient noise, using a vertical and a horizontal linear array of sensors. In the proposed method, noise is modeled as a Gaussian mixture. Initial estimates of the unknown parameters (source coordinates, signal waveforms and noise parameters) are obtained by known/conventional methods, and a generalized expectation maximization algorithm is used to update the initial estimates iteratively. Simulation results indicate that convergence is reached in a small number of (≤10) iterations. Initialization requires one 2-D search and one 1-D search, and the iterative updates require a sequence of 1-D searches. Therefore the computational complexity of the SAGE-USL algorithm is lower than that of conventional techniques such as 3-D MUSIC by several orders of magnitude. We also derive the Cramér–Rao Bound (CRB) for 3-D localization of multiple sources in a range-independent ocean. Simulation results are presented to show that the root-mean-square localization errors of SAGE-USL are close to the corresponding CRBs and significantly lower than those of 3-D MUSIC.

Most existing speech source separation algorithms have been developed for separating sound mixtures acquired by using a conventional microphone array. In contrast, little attention has been paid to the problem of source separation using an acoustic vector sensor (AVS). We propose a new method for the separation of convolutive mixtures by incorporating the intensity vector of the acoustic field, obtained using spatially co-located microphones which carry the direction of arrival (DOA) information. The DOA cues from the intensity vector, together with the frequency bin-wise mixing vector cues, are then used to determine the probability of each time-frequency (T-F) point of the mixture being dominated by a specific source, based on the Gaussian mixture models (GMM), whose parameters are evaluated and refined iteratively using an expectation-maximization (EM) algorithm. Finally, the probability is used to derive the T-F masks for recovering the sources. The proposed method is evaluated in simulated reverberant environments in terms of signal-to-distortion ratio (SDR), giving an average improvement of approximately 1:5 dB as compared with a related T-F mask approach based on a conventional microphone setting.

This paper takes into account different characteristics of TCP-based MIMO sub-channels, including bit error rate (BER) and data rate, in addition to exploiting these differences by implementing sub-channel re-routing methods to improve overall goodput. We show that even simple packet routing mechanisms can provide an overall performance improvement when MIMO sub-channels have unequal error rate. Two scenario models are created: the first one is called a dual parallel-link scenario model where two nodes communicate over two wireless sub-channels, and the second scenario is a more interesting model named the quad-parallel independent link model (Quad-PILM). Simulation are derived for both models, enhanced with two novel switching methods SWS and PLD and two retry scenarios set up: RDL and RRL, in which showing how re-transmissions will be carried out. We investigate the system in the light of goodput under various channel differences. Based on our analysis, we determine the degree of improvement of our proposed models, and show that these techniques may be advantageous for multiple antenna systems. Index Terms ?? Channel difference, MIMO sub-channels, packet re-routing, switching methods.

In the past, both theoretical work and practical implementation of particle filtering (PF) method have been extensively studied. However, its application in underwater signal processing has received much less attention. This paper intends to introduce PF approach for underwater acoustic signal processing. Particularly, we are interested in direction of arrival (DOA) estimation using PF. A detailed introduction along with this perspective is presented in this paper. Since the noise usually spreads the mainlobe of likelihood function and causes problem in subsequent particle resampling step, an exponential weighted likelihood model is developed to emphasize particles at more relevant area. Hence, the the effect due to background noise can be reduced. Real underwater acoustic data collected in SWELLEx-96 experiment are employed to demonstrate the performance of the proposed PF approaches for underwater DOA tracking.

This paper presents a novel method to perform inner product computation based on the distributed arithmetic principles. The input data are represented in the residue domain and are encoded using the thermometer code format while the output data are encoded in the one-hot code format. Compared to the conventional distributed arithmetic based system using binary coded format to represent the residues, the proposed system using the thermometer code encoded residues provides a simple means to perform the modular inner products computation due to the absence of the 2 modulo operation encountered in conventional binary code encoded system. In addition, the modulo adder used in the proposed system can be implemented using simple shifter based circuit utilizing one-hot code format. As there is no carry propagation involved in the addition using one-hot code, while the modulo operation can be performed automatically during the addition process, the operating speed of the one-hot code based modulo adder is much superior compared to the conventional binary code based modulo adder. As inner product is used extensively in FIR filter design, SPICE simulation results for an FIR filter implemented using the proposed system is also presented to demonstrate the validity of the proposed scheme.

In this paper, a nonlinear suboptimal detector whose performance in heavy-tailed noise is significantly better than that of the matched filter is proposed. The detector consists of a nonlinear wavelet denoising filter to enhance the signal-to-noise ratio, followed by a replica correlator. Performance of the detector is investigated through an asymptotic theoretical analysis as well as Monte Carlo simulations. The proposed detector offers the following advantages over the optimal (in the Neyman-Pearson sense) detector: it is easier to implement, and it is more robust with respect to error in modeling the probability distribution of noise.

In this paper, we consider the problem of three-dimensional (3D - azimuth, elevation/depth and range) localization of a single acoustic source using an array of acoustic vector sensors (AVS). Localization algorithms such as multiple signal classification (MUSIC) require a 3D search over the location parameter space which is computationally expensive. Several methods have been proposed based on arrays of acoustic pressure sensors (APS) [1], [2] which simplified this search to polynomial rooting (PR) combined with a 2D search in the range-elevation/depth space. However, the computational complexity remains high for such methods since 2D search is involved. We present a method to localize a source with an array of AVS via a decoupled estimation of azimuth, and a 1D range search combined with PR to estimate the elevation/depth. This is computationally simpler than, and performs as well as PR with 2D searching and 3D MUSIC.

This paper presents the formulation and performance analysis of four techniques for detection of a narrowband acoustic source in a shallow range-independent ocean using an acoustic vector sensor (AVS) array. The array signal vector is not known due to the unknown location of the source. Hence all detectors are based on a generalized likelihood ratio test (GLRT) which involves estimation of the array signal vector. One non-parametric and three parametric (model-based) signal estimators are presented. It is shown that there is a strong correlation between the detector performance and the mean-square signal estimation error. Theoretical expressions for probability of false alarm and probability of detection are derived for all the detectors, and the theoretical predictions are compared with simulation results. It is shown that the detection performance of an AVS array with a certain number of sensors is equal to or slightly better than that of a conventional acoustic pressure sensor array with thrice as many sensors.

This paper considers the problem of three-dimensional (3-D, azimuth, elevation, and range) localization of a single source in the near-field using a single acoustic vector sensor (AVS). The existing multiple signal classification (MUSIC) or maximum likelihood estimation (MLE) methods, which require a 3-D search over the location parameter space, are computationally very expensive. A computationally simple method previously developed by Wu and Wong (IEEE Trans. Aerosp. Electron. Syst. 48(1):159–169, 2012), which we refer to as Eigen-value decomposition and Received Signal strength Indicator-based method (Eigen-RSSI), was able to estimate 3-D location parameters of a single source efficiently. However, it can only be applied to an extended AVS which consists of a pressure sensor separated from the velocity sensors by a certain distance. In this paper, we propose a uni-AVS MUSIC (U-MUSIC) approach for 3-D location parameter estimation based on a compact AVS structure. We decouple the 3-D localization problem into step-by-step estimation of azimuth, elevation, and range and derive closed-form solutions for these parameter estimates by which a complex 3-D search for the parameters can be avoided. We show that the proposed approach outperforms the existing Eigen-RSSI method when the sensor system is required to be mounted in a confined space.

The posterior Cramer Rao lower bound (PCRLB) has recently been proposed as an effective selection criteria for sensor resource management in large, geographically distributed sensor networks. Existing algorithms (in particular the decentralized approaches with no central fusion centre) designed for computing the PCRLB are based on raw observations resulting in significant communication overhead from the sensor nodes to the associated local processing nodes. The paper derives distributive computational techniques for determining the PCRLB for quantized sensor networks configured using decentralized architectures. We refer to the distributed computation of the PCRLB as dPCRLB. The main contribution of the paper is extending the dPCRLB algorithm [1] to quantized observations that leads to significant savings in the communication overhead over its counterparts that use raw observations. In our Monte Carlo simulations, we show that the proposed dPCRLB closely follows the centralized bound based on quantized observations. As expected, there is potential performance loss with quantization as is illustrated by the difference between the dPCRLBs computed using raw and quantized observations. The drop in the estimator's performance is, however, compensated for with an increase in the number of quantization levels associated with the observation quantizer.

This paper considers acoustic source tracking in a room environment using a distributed microphone pair network. Existing time-delay of arrival (TDOA) based approaches usually require all received signals to be transmitted to central processor and synchronized to extract the TDOA measurements. The source positions are then obtained by using a subsequent localization or tracking approach. In this paper, we propose a distributed particle filtering (PF) approach to track the source using a microphone pair network. Each node is constructed by a microphone pair and TDOA measurements are extracted at local nodes. An extended Kalman filter based PF is developed to estimate the first order and the second order statistics of the source state. A consensus filter is then applied to fuse these local statistics between neighboring nodes to achieve a global estimation. Under such an approach, only the state statistics need to be transmitted and the received signals need only to be pairwise synchronized. Consequently, both communication and computational cost can be significantly reduced. Simulations under different reverberant environments demonstrate that the proposed approach outperforms the centralized sequential importance sampling based PF approach in single source tracking as well as in non-concurrent multiple source tracking.

The conditional posterior Cramer-Rao lower bound (PCRLB) is an effective sensor resource management criteria for large, geographically distributed sensor networks. Existing algorithms for distributed computation of the PCRLB (dPCRLB) are based on raw observations leading to significant communication overhead to the estimation mechanism. This letter derives distributed computational techniques for determining the conditional dPCRLB for quantized, decentralized sensor networks (CQ/dPCRLB). Analytical expressions for the CQ/dPCRLB are derived, which are particularly useful for particle filter-based estimators. The CQ/dPCRLB is compared for accuracy with its centralized counterpart through Monte-Carlo simulations.

This paper presents the formulation and analysis of some methods for narrowband detection of underwater acoustic sources in impulsive noise using an array of acoustic vector sensors. Since the array signal vector is unknown due to the unknown location of the source, detection is based on the generalized likelihood ratio test which involves estimation of the signal vector. Different detectors use different signal models which yield different signal estimators. It is shown that the truncated subspace detector (TSD), which uses a truncated normal mode model, yields the best performance.

The invention of cognitive radio concept is aimed to overcome the spectral scarcity issues of emerging radio systems by exploiting under-utilization of licensed spectrum. In cognitive radio networks, unlicensed users, which are referred to as cognitive radios (CRs), are allowed to dynamically access the frequency bands when licensed users (primary users) are inactive. Determining how to allocate unused frequency bands among CRs is one of the most important problems in cognitive radio networks. In this paper, a non-cooperative game theoretic framework to jointly allocate power and frequency bands among CRs in cognitive radio networks is proposed. The proposed game is proved to be an exact potential game, which will finally converge to Nash equilibrium by following the best response dynamics. Determining the best response strategy of each cognitive radio based on the observed opponent strategies is also provided in this paper. From the simulation, it can be seen that the resources have been shared fairly among all CRs.