We analyze diversity performance of scalar fixed- gain amplify-and-forward (AF) cooperation in multiple-input multiple-output (MIMO) relay channels with ns source antennas, nR relay antennas, and nD destination antennas. We first derive the exact symbol error probability (SEP) for maximum likelihood decoding of orthogonal space-time block codes with M-ary phase-shift keying modulation over such channels and then characterize the effect of MIMO cooperative diversity on SEP behavior in a high signal-to-noise ratio regime. We show that the simple scalar-gain AF cooperation can create the diversity order d_AF les n_Sn_D + n_Sn_Rn_D/max{n_S, n_R, n_D} with the equality if 2max{n_S, n_R,n_D} ges n_S + n_R + n_D - 1. This finding reveals that the number of relay antennas greater than or equal to n_S + n_D - 1 is required to achieve the additional diversity order n_Sn_D by scalar-gain AF relaying. We also present the asymptotic SEP as the number of relay antennas tends to infinity.

This paper investigates resource allocation for multi-homing networks where users can simultaneously transmit data to multiple radio access networks (RANs) using multiple air interfaces. We aim at optimally assigning the bandwidth and power to each user-RAN connection so as to maximize energy-efficiency of the entire network subject to user specific QoS requirements as well as the available resource budgets. First, we study the problem of resource allocation for scenarios where the connections between the users and the RANs are predefined, which naturally leads to a fractional program. To obtain the optimal solution efficiently and facilitate distributed implementations, we further equivalently transform the design problem into a convex program using a parameter-free approach and develop a decentralized algorithm based on the alternating direction method of multipliers. Second, we investigate the problem of joint link selection and resource allocation for energy-efficiency maximization. The problem is cast as a mixed integer nonlinear convex program for which we particularize the branch and bound method to find an optimal solution. Then, two suboptimal low-complexity designs are proposed: the first one is based on greedy algorithm, which gradually maximizes the virtual link energy-efficiency; the other one is based on the concept of sparsity-inducing norm. Simulation results are presented to demonstrate the potential gains in terms of energy-efficiency of the proposed methods.

This paper investigates the benefits of applying fountain codes (FCs) in improving the transmission efficiency in broadcasting systems. Particularly, an exact expression of the transmission efficiency for wireless broadcast using FCs is derived. This derivation allows us to compare the transmission efficiency of the fountain code approach (FCA) and network coding approach (NCA) in wireless broadcast. The numerical results demonstrate that FCA achieves better performance than NCA when the number of users is large and vice versa when the number of users is small.

We consider small-cell networks with multiple-antenna transceivers and base stations (BSs) cooperating to jointly design linear precoders to maximize the network energy efficiency, subject to a sum power and per-antenna power constraints at individual BSs, as well as user-specific quality of service (QoS) requirements. Assuming zero-forcing precoding, we formulate the problem of interest as a concave-convex fractional program to which we proposed a centralized optimal solution based on the prevailing Dinkelbach algorithm. To facilitate distributed implementations, we transform the design problem into an equivalent convex program using Charnes-Cooper's transformation. Then, based on the framework of alternative direction method of multipliers (ADMM), we develop a decentralized algorithm, which is numerically shown to achieve fast convergence. Since BSs are generally power-hungry, it may be more energy-efficient if some BSs can be shut down, while still satisfying the QoS constraints. Toward this end, we investigate the problem of joint precoder design and BS selection, which is a mixed Boolean nonlinear program, and then provide an optimal solution by customizing the branch-and-bound method. For real-time applications, we propose a greedy algorithm which achieves near-optimal performance in polynomial time. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms.

Video-based human activity recognition (HAR) means the analysis of motions and behaviors of human from the low level sensors. Over the last decade, automatic HAR is an exigent research area and is considered a significant concern in the field of computer vision and pattern recognition. In this paper, we have presented a robust and an accurate activity recognition system called WS-HAR that consists of wavelet transform coupled with stepwise linear discriminant analysis (SWLDA) followed by hidden Markov model (HMM). Symlet wavelet has been employed in order to extract the features from the activity frames. The most prominent features were selected by proposing a robust technique called stepwise linear discriminant analysis (SWLDA) that focuses on selecting the localized features from the activity frames and discriminating their class based on regression values (i.e., partial F-test values). Finally, we applied a well-known sequential classifier called hidden Markov model (HMM) to give the appropriate labels to the activities. In order to validate the performance of the WS-HAR, we utilized two publicly available standard datasets under two different experimental settings, n–fold cross validation scheme based on subjects; and a set of experiments was performed in order to show the effectiveness of each approach. The weighted average recognition rate for the WS-HAR was 97% across the two different datasets that is a significant improvement in classication accuracy compared to the existing well-known statistical and state-of-the-art methods.