In localization, an outage occurs if the positioning mean squared error (MSE) exceeds a pre-defined threshold εth. For time-of-arrival-based localization, a key factor affecting the MSE is the relative positions of the anchors with respect to the target location. From a design point of view, characterizing the distribution of the MSE over an ensemble of anchor locations as seen from the perspective of a target is essential for providing probabilistic performance guarantees against outage. To solve this difficult problem, previous works have assumed all anchor-target links to have the same SNR (i.e., SNR homogeneity), which neglects the impact of link distance variation on the SNR and the positioning error; for instance, under an inverse-square law pathloss model, the outage probability can differ by orders of magnitude when compared with the homogeneous SNR assumption. In this paper, we derive an approximate expression for the MSE distribution under an inverse-square law pathloss model when the anchors are uniformly distributed around a target. Through simulations, we verify that our approximation can be used to estimate the number of anchors needed so that the outage probability is below 1%.

Localization using the time-of-arrival (ToA) of a wideband signal has the potential to achieve high accuracy, thereby making it a promising choice for a variety of applications. However, a major challenge impacting localization accuracy is multipath propagation, i.e., the existence of indirect paths from a target to an anchor (transceiver) via one or more obstacles present in the environment. In this paper, we provide an overview of the techniques proposed in the literature to analyze and address the effects of multipath on localization accuracy. For this purpose, we first cast the localization of one or more targets as a maximum a priori estimation problem, where the distribution of the amplitudes and ToAs of the indirect paths serves as a prior. Under this framework, we show that multipath can either be a blessing or a curse depending on the extent of prior knowledge available about the multipath statistics. Specifically, in the absence of any indirect path information, only the direct paths that go straight from a target to an anchor contain useful position information; in other words, multipath negatively impacts localization performance. Thus, in this case, it is important to detect the anchors having line-of-sight to the target(s), and we review the techniques reported in the literature to solve this problem. On the other hand, if complete indirect path information is available (i.e., the propagation paths of all the multipath components are known), then each indirect path is equivalent to a direct path from a virtual anchor, and hence, the spatial diversity offered by multipath can be exploited for improving localization accuracy.

In localization applications, the line-of-sight between anchors and targets may be blocked by obstacles in the environment. If an insufficient number of anchors are visible (i.e., have line-of-sight) to a target, then the target cannot be unambiguously localized and is, therefore, said to be in a blind spot. In this paper, we analyze the blind spot probability of a typical target by using stochastic geometry to model the randomness in the obstacle and anchor locations. In doing so, we handle correlated anchor blocking induced by the obstacles, unlike previous works that assume independent anchor blocking. We first characterize the regime over which the independent blocking assumption underestimates the blind spot probability of the typical target, which in turn is characterized as a function of the distribution of the visible area surrounding the target location. Since this distribution is difficult to exactly characterize, we formulate the nearest two-obstacle approximation, which is equivalent to considering correlated blocking for only the nearest two obstacles from the target and assuming independent blocking for the remaining obstacles. Based on this, we derive an approximate expression for the blind spot probability, which helps to determine the anchor deployment intensity needed for the blind spot probability of a typical target to be bounded above by a threshold, μ.

This paper addresses the problem of localizing an unknown number of targets, all having the same radar signature, by a distributed MIMO radar consisting of single antenna transmitters and receivers that cannot determine directions of departure and arrival. Furthermore, we consider the presence of multipath propagation and the possible (correlated) blocking of the direct paths (going from the transmitter and reflecting off a target to the receiver). In its most general form, this problem can be cast as a Bayesian estimation problem where every multipath component is accounted for. However, when the environment map is unknown, this problem is ill-posed and hence, a tractable approximation is derived where only direct paths are accounted for. In particular, we take into account the correlated blocking by scatterers in the environment which appears as a prior term in the Bayesian estimation framework. A sub-optimal polynomial-time algorithm to solve the Bayesian multi-target localization problem with correlated blocking is proposed and its performance is evaluated using simulations. We found that when correlated blocking is severe, assuming the blocking events to be independent and having constant probability (as was done in previous papers) resulted in poor detection performance, with false alarms more likely to occur than detections.

Auto-correlation receivers, such as frequency shift reference systems, offer a low-complexity alternative to Rake receivers, for low data-rate ultra-wideband applications. In such systems, a data signal and a reference signal are simultaneously transmitted. At the receiver, the received signal corresponding to the data is correlated with the received signal corresponding to the reference signal, thereby accumulating the multi-path channel energies in-phase. For improving bandwidth and energy efficiency, multi- differential schemes have also been proposed where multiple data signals share the same reference signal. With the aim of further boosting the achievable data rates, in this work we propose a multi-differential frequency shift reference receiver that supports higher order modulation formats. We design the corresponding receiver architecture that can exploit both the in-phase and quadrature-phase signal components and analytically characterize the signal and noise components. We also study the problem of optimal bit and power allocation to the multiple data signals, both for uncoded and coded systems, as a function of channel metrics that can be easily tracked at the receiver.

Many wireless transceivers, such as massive-MIMO and Rake, are designed to provide high diversity order. In order to reduce cost, low-complexity switched transceivers, e.g., hybrid antenna preprocessing with selection, or S-Rake, which employ a limited number of RF chains or correlators, are popular. This paper shows that the presence of limited number of RF chains or correlators in such transceivers introduces an inherent trade-off between system performance and the channel estimation overhead. We prove that to maximize achievable rate, it is optimal to perform channel estimation for only a subset of diversity branches with the highest second moments, if the diversity branches have the same fading parameters but different mean powers. We also prove that the achievable data-rate is a unimodal function of the size of this subset L, which ensures that any locally optimum L is also globally optimum. A computationally efficient approximation for the ergodic capacity is introduced, which reduces the cost of finding the optimal L significantly. Simulation results for some practically important settings suggest that the optimal choice of L can improve the data rate by a factor of 20-30%.

Media streaming applications have recently attracted a large number of users in the Internet. With the advent of these bandwidth-intensive applications, it is economically inefficient to provide streaming distribution with guaranteed QoS relying only on central resources at a media content provider. Cloud computing offers an elastic infrastructure that media content providers (e.g., Video on Demand (VoD) providers) can use to obtain streaming resources that match the demand. Media content providers are charged for the amount of resources allocated (reserved) in the cloud. Most of the existing cloud providers employ a pricing model for the reserved resources that is based on non-linear time-discount tariffs (e.g., Amazon CloudFront and Amazon EC2). Such a pricing scheme offers discount rates depending non-linearly on the period of time during which the resources are reserved in the cloud. In this case, an open problem is to decide on both the right amount of resources reserved in the cloud, and their reservation time such that the financial cost on the media content provider is minimized. We propose a simple-easy to implement-algorithm for resource reservation that maximally exploits discounted rates offered in the tariffs, while ensuring that sufficient resources are reserved in the cloud. Based on the prediction of demand for streaming capacity, our algorithm is carefully designed to reduce the risk of making wrong resource allocation decisions. The results of our numerical evaluations and simulations show that the proposed algorithm significantly reduces the monetary cost of resource allocations in the cloud as compared to other conventional schemes.

The idea of Distributed Space-Time Codes (DSTCs) was proposed to achieve spatial and temporal diversity without having channel information at the relays in wireless networks. In that scheme, distributive relays cooperate with each other to generate a Space-Time Code (STC) at the receiver. However, DSTCs have poor error rate performances at low signal to noise ratios (SNRs) that may disqualify them from being used in next generation mobile communications systems. To overcome this issue, this paper proposes a novel scheme based on the joint use of DSTCs and turbo codes, which we call “Distributed Space-Time Turbo Codes (DST-TCs)”. We also propose a Fast Forced Symbol Method (FFSM) which significantly improves the coding gain of the proposed DST-TCs. The FFSM is based on repeated decoding operations and does not require any significant change in the design of encoder and decoder of DST-TC. The FFSM provides 0.9 dB of coding gain over simple DST-TC at frame error rate (FER) of 10-5.

We present a framework for designing a class of concatenated codes based on low-density parity-check component codes that we call parallel concatenated Gallager codes (PCGC). The iterative decoding trajectories technique is used to explain the performance of PCGC. Simulation results show an enhanced performance in the practical E/sub b//N/sub 0/ range in both AWGN and flat Rayleigh fading channels. The reduced decoding complexity of the proposed coding scheme along with its good performance makes it an attractive candidate in delay sensitive applications.

We present a class of concatenated codes called parallel concatenated Gallager codes (PCGC) using low-density parity-check (LDPC) component codes. We report simulation results for PCGC that outperform those for comparable LDPC codes both in additive white Gaussian noise (AWGN) and flat Rayleigh fading channels. The good performance of PCGC increases the capacity of CDMA systems while the reduced encoding and decoding complexity of such codes keep the signal processing delays within a reasonable limit.