High speed railways (HSRs) have been deployed widely all over the world in recent years. Different from traditional cellular communication, its high mobility makes it essential to implement power allocation along the time. In the HSR case, the transmission rate depends greatly on the distance between the base station (BS) and the train. As a result, the train receives a time varying data rate service when passing by a BS. It is clear that the most efficient power allocation will spend all the power when the train is nearest from the BS, which will cause great unfairness along the time. On the other hand, the channel inversion allocation achieves the best fairness in terms of constant rate transmission. However, its power efficiency is much lower. Therefore, the power efficiency and the fairness along time are two incompatible objects. For the HSR cellular system considered in this paper, a trade-off between the two is achieved by proposing a temporal proportional fair power allocation scheme. Besides, near optimal closed form solution and one algorithm finding the ǫ-optimal allocation are presented. Index Termshigh speed railway communication, power allocation, channel service, proportional fairness along time.This paper has been published by IEEE Trans. on Veh.

Abstract-Message importance measure (MIM) is an important index to describe the message importance in the scenario of big data. Similar to the Shannon Entropy and Renyi Entropy, MIM is required to characterize the uncertainty of a random process and some related statistical characteristics. Moreover, MIM also need to highlight the importance of those events with relatively small occurring probabilities, thereby is especially applicable to big data. In this paper, we first define a parametric MIM measure from the viewpoint of information theory and then investigate its properties. We also present a parameter selection principle that provides answers to the minority subsets detection problem in the statistical processing of big data.Index Terms-Message importance measure, information theory, big data, Shannon entropy, Renyi entropy.

In the paper, we study the service process $S(t)$ of an independent and identically distributed (\textit{i.i.d.}) Nakagami-$m$ fading channel, which is defined as the amount of service provided, i.e., the integral of the instantaneous channel capacity over time $t$. By using the Characteristic Function (CF) approach and the infinitely divisible law, it is proved that, other than certain generally recognized curve form {or a stochastic process}, the channel service process $S(t)$ is a deterministic linear function of time $t$, namely, $S(t)=c_m^\ast\cdot t$ where $c_m^\ast$ is a constant determined by the fading parameter $m$. Furthermore, we extend it to general \textit{i.i.d.} fading channels and present an explicit form of the constant service rate $c_p^\ast$. The obtained work provides such a new insight on the system design of joint source/channel coding that there exists a coding scheme such that a receiver can decode with zero error probability and zero high layer queuing delay, if the transmitter maintains a constant data rate no more than $c_p^\ast$. Finally, we verify our analysis through Monte Carlo simulations.Comment: 17 pages, 5 figure

We consider a two-way data exchanging system where a master node transfers energy and data packets to a slave node alternatively. The slave node harvests the transferred energy and performs information transmission as long as it has sufficient energy for current block, i.e., according to the best-effort policy. We examine the freshness of the received packets at the master node in terms of age of information (AoI), which is defined as the time elapsed after the generation of the latest received packet. We derive average uplink AoI and uplink data rate as functions of downlink data rate in closed form. The obtained results illustrate the performance limit of the unilaterally powered two-way data exchanging system in terms of timeliness and efficiency. The results also specify the achievable tradeoff between the data rates of the two-way data exchanging system.Index Terms-Age of information, two-way data exchange, wireless power transfer.

To provide stable and high data rate wireless access for passengers in the train, it is necessary to properly deploy base stations along the railway. We consider this issue from the perspective of service, which is defined as the integral of the time-varying instantaneous channel capacity. With large-scale fading assumption, it will be shown that the total service of each base station is inversely proportional to the velocity of the train. Besides, we find that if the ratio of the service provided by a base station in its service region to its total service is given, the base station interval (i.e., the distance between two adjacent base stations) is a constant regardless of the velocity of the train. On the other hand, if a certain amount of service is required, the interval will increase with the velocity of the train. The aforementioned results apply not only to simple curve rails, like line rail and arc rail, but also to any irregular curve rail, provided that the train is traveling at a constant velocity. Furthermore, the new developed results are applied to analyze the on-off transmission strategy of base stations.

Abstract-Message importance measure (MIM) is applicable to characterize the importance of information in the scenario of big data, similar to entropy in information theory. In fact, MIM with a variable parameter can make an effect on the characterization of distribution. Furthermore, by choosing an appropriate parameter of MIM, it is possible to emphasize the message importance of a certain probability element in a distribution. Therefore, parametric MIM can play a vital role in anomaly detection of big data by focusing on probability of an anomalous event. In this paper, we propose a parameter selection method of MIM focusing on a probability element and then present its major properties. In addition, we discuss the parameter selection with prior probability, and investigate the availability in a statistical processing model of big data for anomaly detection problem.

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