Ultra-Reliable Communication for Critical Machine Type Communication via CRAN-Enabled Multi-Connectivity Diversity Schemes

Abstract

This paper focuses on edge-enabled cloud radio access network architecture to achieve ultra-reliable communication, a crucial enabler for supporting mission-critical machine-type communication networks. We propose coordinated multi-point transmission schemes taking advantage of diversity mechanisms in interference-limited downlink cellular networks. The network scenario comprises spatially distributed multiple remote radio heads (RRHs) that may cooperate through silencing, or by using more elaborated diversity strategies such as maximum ratio transmission or transmit antenna selection to serve user equipment in the ultra-reliable operation regime. We derive an exact closed-form expression for the outage probabilities and expected values of signal-to-interference ratio for silencing, transmit antenna selection and maximum ratio transmission schemes. We formulate rate control and energy efficiency under reliability constraints to test the performance and resource usage of the proposed schemes. Furthermore, we study the impact on average system sum throughput with throughput-reliability trade-off under cooperative communication. Extensive numerical analysis shows the feasibility of ultra-reliable communication by implementing diversity schemes with RRHs cooperation.

Multivariate Extreme Value Theory Based Channel Modeling for Ultra-Reliable Communications

“…3) BGPD Model Based on the Logistic Distribution: The bi-logistic family of distributions formulated in ( 11) is used to model the bi-variate extremes based on Theorem 1. Upon obtaining the dependency parameter , as well as the Fréchet transformed variables ˜ and ˜ , we build function (11) and then, by taking the exponential of its negative function, we determine the BGPD as expressed in (10). If this BGPD model is reliable to characterize the tail of the bi-variate distribution, the mean constraint on the probability measure function defined in Theorem 4 should be satisfied.…”

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial

“…Then, the SNR of each link X follows an exponential distribution, 14 i.e., X ∼ Exp(1), and the SNR of the combined received signal can be described as Y = d i =1 X i . For this particular case, the distribution of the sum is known in closed form, i.e., 13 See, for instance, [19] for a comprehensive overview and [6], [100], and [21] for related works and applications. 14 For simplicity of presentation, we assume all links have the same mean, which, in practice, presumes that the nodes are equidistant from the base station, or the devices employ some path-loss compensation.…”

Statistical Tools and Methodologies for URLLC- A Tutorial

“…which, in fact, constitutes our solution for the original problem. Finally, we can obtain a closed-form solution for (22) for the high-SNR regime. Specifically, by using cosh(1/F ) ≈ 1 + 1/(2F 2 ), which comes from the Taylor series expansion, one obtains…”

“…Multi-connectivity emerged as a promising solution for URLLC as it allows the devices to connect to two or more base stations. See for instance [85] for a comprehensive overview and [4], [22], [23] for related works and applications. Herein, we assume a simple system model where a device connects to d ∈ {2, 4, 8, 16} base stations.…”

“…The feasibility of such solution is discussed in, e.g.,[4],[22] 10. For simplicity of presentation, we assume all links have the same mean, which in practice presumes that the nodes are equidistant from the base station, or the devices employ some path-loss compensation.…”