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

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

Energy efficiency-spectral efficiency trade-off of transmit antenna selection

We investigate the energy efficiency-spectral efficiency (EE-SE) trade-off of transmit antenna selection/maximum ratio combining (TAS) scheme. A realistic power consumption model (PCM) is considered, and it is shown that using TAS can provide significant energy savings when compared to multiple-input multiple-output (MIMO) in the low to medium SE region, regardless the number of antennas, as well as outperform transmit beamforming scheme (MRT) for the entire SE range. For a fixed number of receive antennas, our results also show that the EE gain of TAS over MIMO becomes even greater as the number of transmit antennas increases. The optimal value of SE that maximizes the EE is obtained analytically, and confirmed by numerical results. Moreover, the influence of receiver correlation is also evaluated and it is shown that considering a non-realistic PCM can lead to mistakes when comparing TAS and MIMO

On the Area Energy Efficiency of Multiple Transmit Antenna Small Base Stations

We analyze the area energy efficiency (AEE) of spatial multiplexing (SM) and transmit antenna selection (TAS), considering a realistic power consumption model for small base stations (BSs), which includes the power consumed by the backhaul as well as different interference attenuation levels. Our results show an optimum number of BSs for each technique that maximizes the AEE. Moreover, we also show that TAS has a larger AEE than SM when the demand for system capacity is low, while SM becomes more energy efficient when the demanded capacity is larger. Additionally, when the capacity demand and the area to be covered are fixed, the number of BSs needed to be deployed is smaller for SM than for the other techniques. Finally, the system performance in terms of AEE is shown to be strongly dependent on the amount of interference, which in turn depends on the employed interference-mitigation scheme, and on the employed power consumption model

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]

Energy efficiency vs. economic cost of cellular networks under co-channel interference

In this paper we analyze the efficiency of cellular network designs, by taking into account the co-channel interference among cells, different amounts of available bandwidths, and frequency reuse. A realistic power consumption model is considered for the energy efficiency analysis, and for the economic analysis it is employed a model in which the total cost is composed by three factors: spectrum license, energy and infrastructure costs. Our results show that different conclusions can be obtained according to the focus of the network design: energy efficiency or total costs. Assuming an economic point of view, the most cost efficient solutions can be obtained when the number of base stations and the available bandwidth are the factors to be balanced, as the infrastructure cost and the spectrum license costs correspond to the most relevant fraction of the total costs. However, considering the energy efficiency anlysis, it can be more beneficial to employ a higher system bandwidth and balance the number of base stations and the reuse of frequencies in order to minimize the required transmit power

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

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