Energy efficiency of transmit diversity systems under a realistic power consumption model

We compare the downlink energy efficiency of spatial diversity multiple transmit antenna schemes. We determine the minimum required transmit power for a given outage probability. Our analysis shows that antenna selection is in general the most energy efficient option as it requires a single radio-frequency chain. We also investigate the limiting distances up to which the antenna selection technique outperforms the transmit beamforming scheme for different numbers of transmit antennas

Artificial Intelligence for Solar Energy Harvesting in Wireless Sensor Networks

Solar cells have been extensively investigated for wireless sensor networks (WSN). In comparison to other energy harvesting techniques, solar cells are capable of harnessing the highest amount of power density. Furthermore, the energy conversion process does not involve any moving parts and does not require any intermediate energy conversion steps. Their main drawback is the inconsistent amount of energy harvested due to the intermittency and variability of the incoming solar radiation [1]. Consequently, being able to predict the amount of solar radiation is important for making necessary decisions regarding the amount of energy that can be utilized at the sensor node. We demonstrate that artificial intelligence (AI) can be used as an effective technique for predicting the amount of incoming solar radiation at these sensor nodes. We show that a Support Vector Machine (SVM) regression technique can effectively predict the amount of solar radiation for the next 24 hours based on weather data from previous days. We reveal that this technique outperforms other state of the art prediction methods for WSNs. To assess the performance of our proposed solution, we use experimental measurements that were collected for a period of two years from a weather station installed by Beijing Sunda Solar Energy Technology Company [2]. We also demonstrate how the harvested energy can be regulated using an innovative Power Management Unit [3]

Sales Incentives and Sales Performance: The Moderating Effect of Cultural Dimensions

An increasingly globalized world requires businesses to operate across countries and cultures. Managing a business in international markets adds complexity and challenges that can lead to strategic mistakes in dealing with customers, employees and suppliers. Culture impacts many aspects of business. Proper management of culture can lead to competitive advantages. Companies use sales incentives to motivate their sales force and customers in an effort to optimize sales performance. Despite a growing interest in international sales research, few studies address the impact of culture on sales performance. This research will contribute by exploring the moderating effect of culture on sales incentives and sales performance. The dissertation includes a theoretical model based on existing sales literature and Gert Hofstede’s extensive cultural research. Hofstede’s original framework contained four cultural dimensions, which have been extensively researched, though rarely applied to sales performance. Hofstede later added two new dimensions to his original paradigm; this study is the first to test all six dimensions. The dissertation uses sales data from a global company to test the model and attempt to predict the cultural impact on sales incentives and sales performance

Optimizing the Energy Efficiency of Short Term Ultra Reliable Communications in Vehicular Networks

We evaluate the use of HARQ schemes in the context of vehicle to infrastructure communications considering ultra reliable communications in the short term from a channel capacity stand point. We show that it is not possible to meet strict latency requirements with very high reliability without some diversity strategy and propose a solution to determining an optimal limit on the maximum allowed number of retransmissions using Chase combining and simple HARQ to increase energy efficiency. Results show that using the proposed optimizations leads to spending 5 times less energy when compared to only one retransmission in the context of a benchmark test case for urban scenario. In addition, we present an approximation that relates most system parameters and can predict whether or not the link can be closed, which is valuable for system design

Employing Antenna Selection to Improve Energy-Efficiency in Massive MIMO Systems

Massive MIMO systems promise high data rates by employing large number of antennas, which also increases the power usage of the system as a consequence. This creates an optimization problem which specifies how many antennas the system should employ in order to operate with maximal energy efficiency. Our main goal is to consider a base station with a fixed number of antennas, such that the system can operate with a smaller subset of antennas according to the number of active user terminals, which may vary over time. Thus, in this paper we propose an antenna selection algorithm which selects the best antennas according to the better channel conditions with respect to the users, aiming at improving the overall energy efficiency. Then, due to the complexity of the mathematical formulation, a tight approximation for the consumed power is presented, using the Wishart theorem, and it is used to find a deterministic formulation for the energy efficiency. Simulation results show that the approximation is quite tight and that there is significant improvement in terms of energy efficiency when antenna selection is employed.Comment: To appear in Transactions on Emerging Telecommunications Technologies, 12 pages, 8 figures, 2 table

Energy efficiency of some non-cooperative, cooperative and hybrid communication schemes in multi-relay WSNs

In this paper we analyze the energy efficiency of single-hop, multi-hop, cooperative selective decode-and-forward, cooperative incremental decode-and-forward, and even the combination of cooperative and non-cooperative schemes, in wireless sensor networks composed of several nodes. We assume that, as the sensor nodes can experience either non line-of-sight or some line-of-sight conditions, the Nakagami-m fading distribution is used to model the wireless environment. The energy efficiency analysis is constrained by a target outage probability and an end-to-end throughput. Our results show that in most scenarios cooperative incremental schemes are more energy efficient than the other methods

On the Impact of HARQ on the Throughput and Energy Efficiency Using Cross-Layer Analysis

This paper studies the potential improvements in terms of energy efficiency and system throughput of a hybrid automatic retransmission request (HARQ) mechanism. The analysis includes both the physical (PHY) and medium access (MAC) layers. We investigate the trade-off provided by HARQ, which demands reduced transmit power for a given target outage probability at the cost of more accesses to the channel. Since the competition for channel access at the MAC layer is very expensive in terms of energy and delay, our results show that HARQ leads to great performance improvements due to the decrease in the number of contending nodes – a consequence of the reduced required transmit power. Counter-intuitively, our analysis leads to the conclusion that retransmissions may decrease the delay, improving the system performance. Finally, we investigate the optimum values for the number of allowed retransmissions in order to maximize either the throughput or the energy efficiency