In this paper, we investigate the simultaneous wireless information and power transfer (SWIPT) for the two-hop Multiple-Input Multiple-Output (MIMO) Amplify-and-Forward (AF) relay communication systems with the multiantenna energy harvesting relay. We derive the optimal source and relay covariance matrices to characterize the achievable region between the sourcedestination rate and the harvested energy at the relay, namely Rate-Energy (R-E) region. In this context, we consider the ideal scenario where the energy harvester (EH) receiver and the information decoder (ID) receiver at the relay can simultaneously decode the information and harvest the energy at the relay. Then, we consider more practical schemes which are the power splitting (PS) and the time switching (TS) which separate the EH and ID transfer over the power domain and the time domain, respectively.
We propose in this paper a new method to compute the characteristic function (CF) of generalized Gaussian (GG) random variable in terms of the Fox H function. The CF of the sum of two independent GG random variables is then deduced. Based on this results, the probability density function (PDF) and the cumulative distribution function (CDF) of the sum distribution are obtained. These functions are expressed in terms of the bivariate Fox H function. Next, the statistics of the distribution of the sum, such as the moments, the cumulant, and the kurtosis, are analyzed and computed. Due to the complexity of bivariate Fox H function, a solution to reduce such complexity is to approximate the sum of two independent GG random variables by one GG random variable with suitable shape factor. The approximation method depends on the utility of the system so three methods of estimate the shape factor are studied and presented .
The outage capacity (OC) is among the most important performance metrics of communication systems over fading channels. The evaluation of the OC, when equal gain combining (EGC) or maximum ratio combining (MRC) diversity techniques are employed, boils down to computing the cumulative distribution function (CDF) of the sum of channel envelopes (equivalently amplitudes) for EGC or channel gains (equivalently squared enveloped/ amplitudes) for MRC. Closed-form expressions of the CDF of the sum of many generalized fading variates are generally unknown and constitute open problems. We develop a unified hazard rate twisting Importance Sampling (IS) based approach to efficiently estimate the CDF of the sum of independent arbitrary variates. The proposed IS estimator is shown to achieve an asymptotic optimality criterion, which clearly guarantees its efficiency. Some selected simulation results are also shown to illustrate the substantial computational gain achieved by the proposed IS scheme over crude Monte Carlo simulations.
Sboui, Lokman; Rezki, Zouheir; Salem, Ahmed Sultan; Alouini, Mohamed-Slim(2016-01-06)[Poster]
Two major issues are facing today s wireless communications evolution: -Spectrum scarcity: Need for more bandwidth. As a solution, the Cognitive Radio (CR) paradigm, where secondary users (unlicensed) share the spectrum with licensed users, was introduced. -Energy consumption and CO2 emission: The ICT produces 2% of global CO2 emission (equivalent to the aviation industry emission). The cellular networks produces 0.2%. As solution energy efficient systems should be designed rather than traditional spectral efficient systems. In this work, an energy efficient power allocation framework based on maximizing the average EE per parallel channel is presented.
An impulsive noise environment is used in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction. The minimized objective function is defined using the Lp norm. The results are presented and the efficiency of each algorithm is discussed.
In energy harvesting communications, the transmitters have to adapt transmission to availability of energy harvested during the course of communication. The performance of the transmission depends on the channel conditions which vary randomly due to mobility and environmental changes. In this work, we consider the problem of power allocation taking into account the energy arrivals over time and the degree of channel state information (CSI) available at the transmitter, in order to maximize the throughput. In this work, the CSI at the transmitter is not perfect and may include estimation errors. We solve this problem with respect to the causality and energy storage constraints. We determine the optimal offline policy in the case where the channel is assumed to be perfectly known at the receiver. Different cases of CSI availability are studied for the transmitter. We obtain the power policy when the transmitter has either perfect CSI or no CSI. We also investigate of utmost interest the case of fading channels with imperfect CSI. Furthermore, we analyze the asymptotic average throughput in a system where the average recharge rate goes asymptotically to zero and when it is very high.
When marrying randomized distributed space-time coding (RDSTC) to geographical routing, new performance horizons can be created. In order to reach those horizons however, routing protocols must evolve to operate in a fully distributed fashion. In this letter, we expose a technique to construct a fully distributed geographical routing scheme in conjunction with RDSTC. We then demonstrate the performance gains of this novel scheme by comparing it to one of the prominent classical schemes.
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