A study on further properties of wireless channel capacity

Abstract

Future wireless communication calls for exploration of more efficient use of wireless channel capacity to meet the increasing demand on higher data rate and less latency. However, while the ergodic capacity and instantaneous capacity of a wireless channel have been extensively studied, they are in many cases not sufficient for use in assessing if data transmission over the channel meets the quality of service (QoS) requirements. To address this limitation, we advocate a set of wireless channel capacity concepts, namely "cumulative capacity", "maximum cumulative capacity", "minimum cumulative capacity", and "range of cumulative capacity", and for each, study its properties by taking into consideration the impact of the underlying dependence structure of the corresponding stochastic process. Specifically, their cumulative distribution function (CDFs) are investigated extensively, where copula is adopted to express the dependence structures. Results considering both generic and specific dependence structures are derived. In particular, in addition to i.i.d., a specially investigated dependence structure is comonotonicity, i.e, the time series of wireless channel capacity are increasing functions of a common random variable. Appealingly, copula can serve as a unifying technique for obtaining results under various dependence assumptions, e.g. i.i.d. and Markov dependence, which are widely seen in stochastic network calculus. Moreover, some other characterizations of cumulative capacity are also studied, including moment generating function, Mellin transform, and stochastic service curve. With these properties, we believe QoS assessment of data transmission over the channel can be further performed, e.g. by applying analytical techniques and results of the stochastic network calculus theory.

QoS provisioning in large wireless networks

“…If C i and C j , i = j, are not independent, the exact calculation of the service rate distribution seems to be highly involved. For that, we resort to the Fréchet-Hoeffding bounds on copulas [15], [16], which give that cdf of the cumulative capacity satisfies…”

On power and quality of service tradeoff in device-to-device communication

“…In order to find a proper lower bound of the stochastic service curve, we consider the independent case [11], i.e., C(i) and C(j), i = j, are independent, moreover, we assume S(s, t) …”

“…One is that the present paper focuses on the power-QoS tradeoff, while [3] focuses more on the maximum battery life span. Another is that the stochastic service curve, a key concept [8]- [10] used by both papers, of the channel is derived directly from the CDF of the wireless channel cumulative capacity [11] in the present paper, while in [3] it is based on a Gilbert-Elliott (GE) channel mapped from an underlying Rayleigh fading channel. Furthermore, in obtaining the stochastic service curve, the present paper adopts a copula technique, while [3] relies on the GE analysis.…”

“…The analysis makes use of the stochastic network calculus theory [8]- [10]. More specifically, we first introduce the concept of cumulative capacity function of the channel [11]. Then, bounds on the cumulative distribution function (CDF) of the cumulative capacity is derived by making use of the copula theory [12], [13], which transforms the demand on finding a joint distribution function for the CDF to simple functions of the marginal distribution function of instantaneous capacity.…”