Wireless devices have been used to investigate the environment and to understand our physical world. In this work, we undertake the challenging problem of identifying location of obstacles and objects by WiFi signals. Gathering wireless sensory data to form an image is difficult since wireless signals are susceptible to multipath. Moreover, reconstructing an image of unknown objects based on the measurements of sparse signals is an ill-posed problem. To tackle these problems, we first present a linear model using received signal strength indicator (RSSI) measurements. We define the sparse beamforming problem as an ℓ0-norm optimization problem, then use the iterative reweighted ℓ1heuristic algorithm to obtain an optimal solution as a multipath. Finally, the multipath fading is removed by using Machine Learning. More specifically, we use Support Vector Regression (SVR) to identify a clear image of the unknown object. Our results show that the proposed method can reconstruct signals as a 3D image with a satisfactory visual appearance, i.e. the generated data mesh is well defined and smooth compared to previous work.

Use of wireless signal for identification of unknown object, or technology to see-through a wall to form an image, is gaining growing interest from various fields including law enforcement and military sectors, disaster management, or even in civilian sectors such as construction sites. The great challenge in the implementation of such technology is the stochastic disturbances on wireless signal which will result in a signal with missing samples. Compressive Sensing (CS) is a powerful tool for estimating the missing samples since it can find accurate solution to largely underdetermined linear wireless signals. However, sparse models like CS can also suffer from information loss dues to stochastic lossy nature of wireless, making CS not to have accurate information for reconstruction of a signal. In this paper, we developed a theoretical and experimental framework for the mapping of obstacles by reconstructing the wireless signal based on a sparse signal. We apply tensor format to perform the computations along each mode by relaxing the tensor constraints to obtain accurate results. The proposed framework demonstrates how to take 2D signals, formulate estimate signals and produce a 3D image location in a completely unknown area inside of the obstacle (wall).

In this paper, we developed a theoretical and experimental framework for the mapping of obstacles using WiFi, based on a small number of wireless channel samples. This is very challenging due to the numerous channel coefficients to be estimated over the time-varying channel and the channel estimation of a wireless signal transmission to be considered for compressive sampling. In a typical communication system, the signal is sampled at least twice at the highest frequency contained in the signal. However, this limits efficient ways to compress the signal, as it places a huge burden on sampling the entire signal while only a small number of the transform coefficients are needed to represent the signal. To tackle this problem, we will focused on a mathematical optimization problem for the most efficient compressed sensing method called ℓ1-norm, known as Basis Pursuit. Before optimizing the problem, the noise was removed from the signal, namely, multipath fading. Our experimental results show the improved performance in the number of iterations for obtaining a framework for the mapping of obstacles.

Wireless radio-frequency (RF) technologies has had universal wide-scale deployment. Despite this, the practical limitations of the RF spectrum being unable to meet the challenges (e.g. lack of security, high interference, limited bandwidth, scalability) of RF based communication networks have become increasingly apparent over the past decade. With the ever-growing of data heavy wireless communications, especially on the last mile (e.g. wireless mesh network) and the backbone, methods are required to help addresses the problem RF have been facing for the next generation network. Faced with such daunting obstacles in RF-only networks, the use of Free Space Optical (FSO) for wireless communications was proposed. FSO is a promising solution to security, limited spectrum bandwidth, the scalability problem of wireless mesh networks, but also the advantage of large transmission distance, free license, interference immunity, and high-bandwidth. Despite the major advantages of FSO technology, its widespread use has been hampered by atmospheric turbulence-induced fading. However, FSO is still consider to be a practical solution to RF challenges. To maximize the potential of FSO networks, we study the problem of maximum path resource flow for a route and minimum linear delay while the FSO topology under atmospheric turbulence. Such methods can be easy adopted to SDN to manage the data flow at the link-or network-layer.

The 4G standard Long Term Evolution (LTE) utilizes discontinuous reception (DRX) to extend the user equipments battery lifetime. DRX permits an idle UE to power off the radio receiver for two predefined sleep period and then wake up to receive the next paging message. Two major basic power saving models proposed to data are the 3GPP ETSI model and industrial DRX model proposed by Nokia. While previous studies have investigated power saving with the 3GPP ETSI models, the industrial DRX model has not been considered for analytical studies to date. Thus, there is a need to optimize the DRX parameters in the industrial model so as to maximize power saving without incurring network reentry and packet delays. In this paper, we take an overview of various static DRX cycles of the LTE/LTE-Advanced power saving mechanisms by modelling the system with bursty packet data traffic using a semi-Markov process. Using this analytical model, we will show the tradeoff relationship between the power saving and wake-up delay performance in the industrial model.

The 4G standard Long Term Evolution (LTE) has been developed for high-bandwidth mobile access for today's data-heavy applications, consequently, a better experience for the end user. Since cellular communication is ready available, LTE communication has been designed to work at high speeds for vehicular communication. The challenge is that the protocols in LTE/LTE-Advanced should not only provide good packet delivery but also adapt to changes in the network topology due to vehicle volume and vehicular mobility. It is a critical requirement to ensure a seamless quality of experience ranging from safety to relieving congestion as deployment of LTE/LTE-Advanced become common. This requires learning how to improve the LTE/LTE-Advanced model to better appeal to a wider base and move toward additional solutions. In this paper we present a feasibility analysis for performing vehicular communication via a queueing theory approach based on a multi-server queue using real LTE traffic. A M/M/m model is employed to evaluate the probability that a vehicle finds all channels busy, as well as to derive the expected waiting times and the expected number of channel switches. Also, when a base station (eNB) becomes overloaded with a single-hop, a multi-hop rerouting optimization approach is presented.