This paper evaluates both the operational third-generation (3G) as well as the emerging fourth-generation (4G) wireless systems and investigates new technologies that might transpire and change things in the future.

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In order to evaluate the performance of new mobile network technologies, system level simulations are crucial. They aim at determining whether, and at which level predicted link level gains impact network performance. In this paper we present a MATLAB computationally efficient LTE system level simulator. The simulator is offered for free under an academic, noncommercial use license, a first to the authors' knowledge. The simulator is capable of evaluating the performance of the Downlink Shared Channel of LTE SISO and MIMO networks using Open Loop Spatial Multiplexing and Transmission Diversity transmit modes. The physical layer model is based on the postequalization SINR and provides the simulation pre-calculated "fading parameters" representing each of the individual interference terms. This structure allows the fading parameters to be pregenerated offline, vastly reducing computational complexity at run-time.

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We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task-based on the past and current measurements of all sensors-using only local processing and local communications with its neighbors. In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms. This "likelihood consensus" method is applicable if the local likelihood functions of the various sensors (viewed as conditional probability density functions of the local measurements) belong to the exponential family of distributions. We then use the likelihood consensus method to implement a distributed particle filter and a distributed Gaussian particle filter. Each sensor runs a local particle filter, or a local Gaussian particle filter, that computes a global state estimate. The weight update in each local (Gaussian) particle filter employs the JLF, which is obtained through the likelihood consensus scheme. For the distributed Gaussian particle filter, the number of particles can be significantly reduced by means of an additional consensus scheme. Simulation results are presented to assess the performance of the proposed distributed particle filters for a multiple target tracking problem.Centralized estimation techniques transmit sensor data to a possibly distant fusion center [1]. This may require energyintensive communications over large distances or complex multi-hop routing protocols, which results in poor scalability. Centralized techniques are also less robust, and less suitable if the estimation results have to be available at the sensors (e.g., in sensor-actuator networks [4]). Furthermore, the fusion center must be aware of the measurement models and, possibly, additional parameters of all sensors. By contrast, decentralized estimation techniques without a fusion center use innetwork processing and neighbor-to-neighbor communications to achieve low energy consumption as well as high robustness and scalability. The sensors do not require knowledge of the network topology, and no routing protocols are needed.There are two basic categories of decentralized estimation techniques. In the first, information is transmitted in a sequential manner from sensor to sensor [5]- [7]. In the second, each sensor diffuses its local information in an iterative process using broadcasts to a set of neighboring sensors (e.g., [8]). This second category is more robust but involves an increased communication overhead. It includes consensusbased estimation techniques, which use distributed algorithms for reaching a consensus (on a sum, average, maximum, etc.) in the network [9], [10]. Examples are gossip algorithms [10], consensus algorithms [11], and combined approaches [12].In this paper, we consider a decentralized wireless sensor network architecture without a fusion center and use con...