In recent years, user cooperative traffic forwarding is a popular study topic and broadly seen as one of the important promising technologies to improve energy efficiency (EE) of the battery-driven mobile terminal (MT). However, the battery-driven devices always suffer from a problem of limited working time due to battery life. In this paper, we propose a simply machine learnable bandwidth allocation strategy for user cooperation-aided wireless communication systems and evaluate the power consumption of the systems via both theoretical and experimental approaches. By using the proposed bandwidth allocation strategy, we first derive the mathematical expressions to evaluate the transmission power of the MTs for non-cooperative and cooperative scenarios by a generalized channel model. In this generalized model, the spatially correlated shadowing and frequency selective fading are considered as channel effects, and this generalized model is mathematically analyzed for the consumed power via the proposed scenarios with the long-term evolution (LTE) power model for smartphones. In the final stage, we evaluate the results by our smartphone test-bed. The results obtained in this paper show that the benefits of the user cooperation-aided traffic forwarding are significant. Unfortunately, according to the numerical analysis, because there are some physical constraints for MTs, such as maximal transmit power, we cannot drastically obtain the benefits in real application cases. Some interesting points, such as how to use a machine learning approach to reduce the system complexity and thus improve transmission performances, are also discussed in this paper.

In recent years, unmanned aerial vehicle (UAV), also called a drone, is getting more and more important in many emerging technology areas. For communication area, the drone also takes an important role in lots of significant topics like emergency communications, device-to-device (D2D) communications, and the Internet of Things (IoT). One of the important drone applications is to collect and share data among drones and other aircraft, which is useful for drone control so that dangerous conditions can be avoided. In particular, the drone control and safety guarantees are difficult to attain, especially, when drones fly beyond the line of sight (BLOS). For this reason, we develop a drone location information sharing system using the 920-MHz band. We use this system to do a long distance propagation field experiment for model establishment. Unfortunately, the current data collection for model establishment work needs a great effort and time to do experiments to collect a huge number of data for data analysis so that a suitable model can be established. Therefore, in this paper, we propose a novel method, which is based on machine learning approach, to data analysis and model establishment for drone communications, so that the effort and cost for establishing model can be reduced and a model, which captures more details about the drone communications, can be obtained. The results of this paper validate that the proposed method can indeed establish a more complicated model with less effort. Specifically, from the distribution of the training error, it can be known that there are over 80% training errors with intensity less than 5, which ensures the error performance of the proposed method.INDEX TERMS Unmanned aerial vehicle (UAV), drone communication, machine learning, Internet of Things (IoT).

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Both acute unilateral nephrectomy (AUN) and acute ureteral pressure elevation (UPE) stimulate sodium excretion (UNaV) from the contralateral kidney, a response which in each case is interrupted by prior denervation of either kidney. Yet the natriuresis after AUN is known to be related to an increase in the plasma concentration of a gamma-melanocyte stimulating hormone (gamma-MSH)-like peptide. In anesthetized rats, sham AUN had no effect on contralateral UNaV, and plasma immunoreactive (IR) gamma-MSH concentration was 10.6 +/- 3.0 (SD) fmol/ml. In rats with intact renal innervation, UNaV more than doubled after AUN (P less than 0.001), and IR-gamma-MSH was increased to 14.9 +/- 4.6 fmol/ml (P less than 0.025). Unilateral renal denervation led to the expected increase in ipsilateral and decrease in contralateral UNaV, and neither sham AUN nor AUN of the denervated of innervated kidney influenced UNaV. In all three of these groups, IR-gamma-MSH concentration was reduced below the sham or post-AUN values seen in rats with innervated kidneys, to 4.9 +/- 3.3, 3.8 +/- 3.4, and 2.8 +/- 3.5 fmol/ml, respectively (P less than 0.001 for all). These results suggested that removal of renal afferent nerve input by renal denervation lowered basal IR-gamma-MSH activity and prevented the stimulated level normally seen after AUN. To examine the effect of stimulating afferent renal nerve activity, we carried out UPE, a maneuver known to increase ipsilateral afferent renal nerve traffic through activation of renal mechanoreceptors, as well as cause a natriuresis from the contralateral kidney.(ABSTRACT TRUNCATED AT 250 WORDS)