ABSTRACT High data rate is one of the main challenges in next generation wireless system in the mobile environment. Mobile Wi-Max system based on IEEE 802.16e standard supports high throughput by implementing Orthogonal Frequency Division Multiple Access (OFDMA). In Mobile environment, channel

Modulation and coding techniques that are both power-and bandwidth-efficient are examined at the system level in this paper. Such techniques include convolutional encoding with QPSK modulation and the relatively new trellis-coded modulation. Power and bandwidth trade-offs are discussed, a sample link calculation is given, and hardware implemen-tation is considered.

In this paper, we present a background and theory of the effect of Surface Acoustic Wave (SAW) Filter Module (SFM) in-band ripple on high data rate communications parameters such as the Er-ror Vector Magnitude (EVM). In addition, we present analyses and statements for the choice of unbalanced S

for high data rate wireless communication. The encoding/decoding complexity of these codes is comparable to trellis codes employed in practice over Gaussian channels. The codes constructed here provide the best tradeoff between data rate, diversity advantage, and trellis complexity. Simulation results

Wireless local area networks (W-LANs) have become increasingly popular due to the recent availability of affordable devices that are capable of communicating at high data rates. These high rates are possible, in part, through new modulation schemes that are optimized for the channel conditions

Abstract. This paper is motivated by the need for fundamental understanding of ultimate limits of bandwidth efficient delivery of higher bit-rates in digital wireless communications and to also begin to look into how these limits might be approached. We examine exploitation of multi-element array

of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large

The pattern of zero entries in the inverse covariance matrix of a multivariate normal distribution corresponds to conditional independence restrictions between variables. Covariance selection aims at estimating those structural zeros from data. We show that neighborhood selection with the Lasso

can last for a significant period. Previous approaches rely on the use of either long range communication which leads to rapid draining of nodes ’ limited batteries, or existing node mobility which results in low data delivery rates and large delays. In this paper, we describe a Message Ferrying (MF

allows any number of heterogeneous clients to acquire bulk data with optimal efficiency at times of their choosing. Moreover, no feedback channels are needed to ensure reliable delivery, even in the face of high loss rates. We develop a protocol that closely approximates a digital fountain using a new