A new choice of penalty function for robust multiuser detection based on M-estimation

In this letter, we propose a new robust MUD, called /spl alpha/ detector, for non-Gaussian noise. We consider the Gaussian-mixture model for non-Gaussian or impulsive noise. Our technique outperforms the decorrelator and the minimax detectors in highly impulsive noise. The proposed method uses a parametric cost function, where the parameter /spl alpha/ is selected using the difference between the asymptotic variance of estimation error of the /spl alpha/ detector and that of the minimax detector.     

A covariance fitting approach to parametric localization of multiple incoherently distributed sources

In this paper, a new algorithm for parametric localization of multiple incoherently distributed sources is presented. This algorithm is based on an approximation of the array covariance matrix using central and noncentral moments of the source angular power densities. Based on this approximation, a new computationally simple covariance fitting-based technique is proposed to estimate these moments. Then, the source parameters are obtained from the moment estimates. Compared with earlier algorithms, our technique has lower computational cost and obtains the parameter estimates in a closed form. In addition, it can be applied to scenarios with multiple sources that may have different angular power densities, while other known methods are not applicable to such scenarios.     

Distributed source localization using ESPRIT algorithm

A new algorithm based on ESPRIT is proposed for the estimation of the central angle and angular extension of distributed sources. The central angles are estimated using TLS-ESPRIT for both incoherently distributed (ID) and coherently distributed (CD) sources. For CD sources, the extension width is estimated by constructing a one-dimensional (1-D) distributed source parameter estimator (DSPE) spectrum for each source. For ID sources, the extension widths are estimated using the central moments of the distribution. The algorithm can be used for sources with different angular distributions     

Localization of wideband signals using least-squares and totalleast-squares approaches

In this paper, we introduce a new focusing technique for localization of wideband signals. Relaxing the unitary assumption for the focusing matrices, we formulate the least-square (LS) and the total least-square (TLS) coherent signal-subspace methods. The TLS is an alternative to the conventional LS and uses the fact that errors can exist both in the focusing location matrix as well as in the estimated location matrix at a given frequency bin. To prevent the focusing loss, we use a class of focusing matrices that are constant under multiplication by their Hermitian transpose. The class of unitary matrices comports with this property. We then develop a new focusing technique based on a modification to the TLS (MTLS). It is shown that the computational complexity of the new technique is significantly lower than that for the rotational signal subspace method (RSS). The focusing gain of the new technique is also larger than the focusing gain of the RSS algorithm. The simulation study shows that, compared with the RSS, the new algorithm has a smaller resolution signal to-noise ratio (SNR)     

Alternative proofs for “On unique localization ofconstrained-signal sources”

We provide simple proofs for the theorems in “On Unique Localization of Constrained-Signal Sources” by M. Wax (see ibid. vol.40, p.1542-1547, June 1992). The approach is based on the topological dimension of a set. All the possible observation matrices form the legitimate set. The observation matrices that can have nonunique solutions form the ambiguity set. The components of the legitimate and ambiguity set are random matrices. We find the conditions under which the dimensionality of the ambiguity set is smaller than the dimensionality of the legitimate set. In such a case, the probability of the ambiguity set is zero and with probability one, a unique solution can be found for the localization problem     

Community-aware single-copy content forwarding in Mobile Social Network

Wireless Networks - One of the most challenging issues in Mobile Social Networks (MSNs) is to design a messages forwarding method that achieves high delivery and low communication overhead....     

Reliable packet transmissions in multipath routed wireless networks

We study the problem of using path diversification to provide low probability of packet loss (PPL) in wireless networks. Path diversification uses erasure codes and multiple paths in the network to transmit packets. The source uses Forward Error Correction (FEC) to encode each packet into multiple fragments and transmits the fragments to the destination using multiple disjoint paths. The source uses a load balancing algorithm to determine how many fragments should be transmitted on each path. The destination can reconstruct the packet if it receives a number of fragments equal to or higher than the number of fragments in the original packet. We study the load balancing algorithm in two general cases. In the first case, we assume that no knowledge of the performance along the paths is available at the source. In such a case, the source decomposes traffic uniformly among the paths; we call this case blind load balancing. We show that for low PPL, blind load balancing outperforms single-path transmission. In the second case, we assume that a feedback mechanism periodically provides the source with information about the performance along each path. With that information, the source can optimally distribute the fragments. We show how to distribute the fragments for minimized PPL, and maximized efficiency given a bound on PPL. We evaluate the performance of the scheme through numerical simulations.     

Anonymous Indoor Navigation System on Handheld Mobile Devices for Visually Impaired

An indoor positioning and navigation system based on measurements of received signal strength in wireless local area network is proposed. In the system, the location determination problem is solved by applying compressive sensing, which offers recovery of sparse signals from a small number of noisy measurements by solving an ?₁-minimization problem. The refined estimate is then used with a map-adaptive Kalman filter for real-time tracking. A navigation module integrated with the tracking system guides users to pre-defined destinations with voice instructions. Experimental results with a system that was implemented on a PDA shows that the proposed tracking system is lightweight so that it can be used on a resource constrained platform while outperforming the widely used traditional positioning and tracking systems. A pilot study was carried out with 30 visually impaired subjects from the Canadian National Institute for the Blind. Testing results show that the proposed system can be used to guide visually impaired subjects to their desired destinations with a very high success rate.     

On the downlink interference in heterogeneous wireless DS-CDMA networks

In this paper, we show that the total downlink interference in heterogeneous wireless DS-CDMA networks follows an asymptotically self-similar (as-s) process. The as-s model is valid for the interference under certain conditions on channel variations and traffic characteristics that cover a range of practical situations. We derive these conditions and generalize earlier results, obtained for data-centric cellular networks, to heterogeneous cellular networks. Simulation results for actual cases confirm analytical results, and show that non-uniform spatial distribution of users and their soft-hand-off status do not affect the nature of this self-similar process. Furthermore, we discuss the impact of the analysis developed in this paper in designing appropriate mechanisms for controlling radio resources in such networks.