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This paper addresses the problem of distributed detection in the case where, under the signal-present hypothesis, the signal-to-noise ratio (SNR) is unknown and/or observations are correlated. We assume that each local detector makes a (binary) decision while meeting a local false alarm constraint; it then transmits its decision to a fusion center. The unknown SNR at each local detector induces an unknown probability of detection and, hence, the optimal detector at the fusion center does not exist. We begin with the case most often considered in the literature: independent observations. In this case, we derive the asymptotically optimal separating function estimation test (AOSFET) and the generalized likelihood ratio test (GLRT). Moreover, we propose a method to set the local false alarm rates to achieve the maximum probability of detection at the fusion center (while meeting a constraint on the global probability of false alarm). The second part of this paper considers the case of correlated observations. We show that the AOSFET for this problem does not exist. As alternatives, we propose three suboptimal SFETs: based on an approximation to the AOSFET, the Kullback–Leibler divergence, and the Euclidean distance of the estimated probability mass function (pmf) of the observations under each hypotheses. Finally, we propose two methods to improve the performance of the estimation of the pmfs using a library of training labeled data based on the maximum likelihood estimation and expected maximization methods. [ABSTRACT FROM AUTHOR]

The recent advancements in digital control of the waveforms transmitted by radar systems has enabled implementation of multiple-input multiple-output radar systems. In such radars, each element is capable of transmitting a different waveform; in this paper, we focus on modifying a multiplicative weight at each transmitter to improve detection performance. Several authors have considered the problem of optimizing the transmit weights and the receive filter generally to maximize the output signal-to-interference-plus-noise ratio. Crucially, these previous works assumed a priori knowledge of the required second-order statistics. In this regard, we make two key contributions: First, we formulate and solve an optimization problem to estimate these required statistics using only signals received in previous coherent processing intervals (CPIs). Essentially, we estimate the transmit interference covariance matrix using receive data. The estimation does not assume any specific structure for the clutter covariance matrix. By using the most recent CPIs in the design, our approach makes it possible to track changes in the interference environment, and, therefore, adaptively design the transmit code in real time. Second, we introduce reduced-dimension processing for transmit adaptivity. As in the case of receive space-time adaptive processing (STAP), it is crucial to be able to perform filter design with only a limited number of available training samples. While reduced-dimension processing is well established for receive adaptivity, as we will see, transmit reduced-dimension processing requires a completely different formulation. We consider transmit code design for a single look angle-Doppler bin and also for a range of Doppler bins in the case that the target Doppler shift is unknown. Finally, we present simulation results, in the context of phase-perturbed propagation and airborne radars, to illustrate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

The distributed power control algorithms available in this paper assume perfect estimation of interference power at the receivers and often, the availability of perfect local channel state information (CSI). However, perfect estimation of interference power or acquisition of channel information is impractical. In this paper, we consider a generic class of distributed power control algorithms and analyze the resulting performance loss when faced with errors in CSI and/or interference level estimation. We approximate such power control algorithms with linearized state space dynamics wherein the errors in the estimation of received interference or the CSI, can be modeled as additive observation noise. Based on the proposed model, we derive lower bounds on the norm of the errors in the transmit power vector and receive signal-to-interference-plus-noise ratio (SINR) vector. Our analysis suggests that when the target SINR vector approaches the Pareto-optimal bound, the errors in the transmit vectors could grow without bound. As a consequence, to maintain a performance loss within an acceptable range, either much longer estimation times or lower target SINRs are required. Our numerical results confirm the validity of our analysis and the validity of the proposed bounds. [ABSTRACT FROM PUBLISHER]

Assuming a network of infinite extent, several researchers have analyzed small-cell networks using a Poisson point process (PPP) location model, leading to simple analytic expressions. The general assumption has been that these results apply to finite-area networks as well. However, do the results of infinite-area networks apply to finite-area networks? In this paper, we answer this question by obtaining an accurate approximation for the achievable signal-to-interference-plus-noise ratio (SINR) and user capacity in the downlink of a finite-area network with a fixed number of access points (APs). The APs are uniformly distributed within the area of interest. Our analysis shows that, crucially, the results of infinite-area networks are very different from those for finite-area networks of low-to-medium AP density. Comprehensive simulations are used to illustrate the accuracy of our analysis. For practical values of signal transmit powers and AP densities, the analytic expressions capture the behavior of the system well. As an added benefit, the formulations developed here can be used in parametric studies for network design. Here, the analysis is used to obtain the required number of APs to guarantee a desired target capacity in a finite-area network. [ABSTRACT FROM AUTHOR]

We consider waveform design for colocated multiple-input, multiple-output (MIMO) radar systems where the signal phase is subject to random phase perturbations during propagation. We formulate an iterative algorithm to obtain both waveform parameters and the space-time adaptive weights to maximize the signal-to-interference-plus-noise ratio (SINR). We ensure that the impact of the chosen waveforms on the interference signal is accounted for when modeling the second-order interference statistics. The waveform design technique is then extended to include peak-to-average power constraints. An example of a clutter and target model is provided to show how the optimal waveform design improves radar detection performance in the presence of the signal phase randomization. [ABSTRACT FROM PUBLISHER]

We propose a four-stage hierarchical resource allocation scheme for the downlink of a large-scale small-cell network in the context of orthogonal frequency-division multiple access (OFDMA). Since interference limits the capabilities of such networks, resource allocation and interference management are crucial. However, obtaining the globally optimum resource allocation is exponentially complex and mathematically intractable. Here, we develop a partially decentralized algorithm to obtain an effective solution. The three major advantages of our work are as follows: 1) as opposed to a fixed resource allocation, we consider load demand at each access point (AP) when allocating spectrum; 2) to prevent overloaded APs, our scheme is dynamic in the sense that as the users move from one AP to the other, so do the allocated resources, if necessary, and such considerations generally result in huge computational complexity, which brings us to the third advantage: 3) we tackle complexity by introducing a hierarchical scheme comprising four phases: user association, load estimation, interference management via graph coloring, and scheduling. We provide mathematical analysis for the first three steps modeling the user and AP locations as Poisson point processes. Finally, we provide the results of numerical simulations to illustrate the efficacy of our scheme. [ABSTRACT FROM PUBLISHER]

Efficient optimization techniques are important to manage interference in emerging dense wireless networks. Here, we address interference management through power control as a general utility maximization problem. For the class of utility functions that are concave in the logarithm of the optimization variables, we propose a power control algorithm based on fixed-point iterations. The iterations converge to the globally optimal power vector. One key benefit is that, for a network with N transmitters and a centralized implementation of the power control algorithm, the computational complexity per iteration of the algorithm is \cal O(N^2). When implemented in a distributed fashion and allowing for a signaling complexity of N messages per iteration, the computation complexity is reduced to \cal O(N). We show that the proposed centralized and distributed versions of the algorithm converge to the optimal power vector at a linear rate. Our numerical results suggest that in most instances, the algorithm takes fewer than ten iterations to converge, even fewer if the initialization is close to the optimal power vector. The proposed algorithm is, therefore, very efficient for power control in slowly fading channels. Furthermore, unlike previous works in the literature, the proposed algorithm does not require the objective function to be separable into a sum of individual utilities. As an example, we present results for power control in a two-hop decode-and-forward cooperative relay network and illustrate the performance gains due to interference management. [ABSTRACT FROM AUTHOR]

In recent years, reconfigurable antennas have been sought to improve the performance of multiple-input multiple-output (MIMO) wireless communication systems. Their ability to dynamically reconfigure their radiation pattern adds diversity in a manner that is not possible with fixed antennas. This paper investigates the performance benefits provided by pattern reconfigurable receiving antennas with uniform beam steering capability. Their potential performance is first estimated using simulations, and for the first time, the effect of uniform beam steering on MIMO system performance is evaluated in a real indoor channel using two electrically steerable passive array radiator (ESPAR) antennas. Performance comparison is made against a pair of monopole antennas using a hardware bit error rate (BER) test-bed that incorporates statistical spatial averaging in order to assess performance improvements in a more realistic way, and analyze the effect of antenna diversity on the overall system performance. The MIMO-ESPAR system reduces BER with certain pattern combinations and excels in capacity evaluations. The ESPAR antennas improve the spatially averaged channel capacity by as much as 37% at 10 dB transmit SNR, and gain an additional 1 bit/s/Hz in peak capacity at 10 dB receive SNR from diversity gain alone. These improvements make pattern reconfigurable antennas promising options in MIMO-related applications. [ABSTRACT FROM PUBLISHER]