Toward a Unified Framework for Analysis of Multi-RAT Heterogeneous Wireless Networks

Abstract

The increased penetration of different radio access technologies (RATs) and the growing trend towards their convergence necessitates the investigation of wireless heterogeneous networks (HetNets) from coverage and capacity perspective. This paper develops a unified framework for signal-to-interference-plus-noise ratio and rate coverage analysis of multi-RAT HetNets, with each RAT employing either a contention-free or a contention-based channel access strategy. The proposed framework adopts tools from stochastic geometry, with the location of APs and mobile users modeled through independent Poisson point processes (PPPs). We specifically focus on a two-RAT scenario (i.e., cellular and Wi-Fi), where for multi-tier Wi-Fi RAT, with contention-based channel access like CSMA/CA, the location dependent distribution of interfering APs has been approximated through a homogeneous PPP. Moreover, by using some simple yet realistic set of assumptions, the distance to nearest active AP has been defined which results in simplified expressions. The medium access probability for a random and a tagged AP under a multi-tier Wi-Fi RAT has also been derived and discussed. By keeping in view the tremendous effect of temporal domain on overall network performance, the stable queue probability has been derived by assuming a non-saturated traffic model. The results have been validated through extensive simulations and compared with existing approaches. Some useful insights have also been presented that shed light on design and analysis of multi-RAT HetNets and provide motivation for further research in this direction.

Development of an AI-Enabled Q-Agent for Making Data Offloading Decisions in a Multi-RAT Wireless Network

“…Furthermore, the states with higher d and larger h are visited less often as evident from Figure 2. Because, if h � 800 Mbits and d � 10 mint at the (1) Initialization (2) π(a|s) as a random uniform policy (3) Q(s, a)⟵Ω(s, a) + 􏽐 ∀a i 􏽐 ∀s ′ π(a i |s)Q(s ′ , a i ) (4) β(s, a)⟵0 ∀s ∈ S, a ∈ A (5) for each download request μ o (ψ, D) -episode do (6) defne state s(k, h, d) -d � D, h � ψ, k randomly generated using (6) (7) while download is not complete (h > 0) do (8) if Q(s, ∀a) is same then (9) choose action a at random (10) else (11) choose a � argmin a Q(s, a) (12) end if (13) take action a (14) update c(s, a) by using ( 13) (15) if d > 0 then (16) obtain Ω(s, a) using ( 9) (17) obtain We have reported the data ofoading policy learned by the Q-agent in Figure 3, that is, the optimal actions taken by the Q-agent in the same states as mentioned in Figure 2. We have included data for only three most important locations wherein the decision-making is challenging just to give better insights.…”

“…For an AP under cellular RAT (r � c), the indicator function is unity because all the APs are assumed to transmit simultaneously. For an AP under Wi-Fi RAT (r � w), it can be either zero or unity because not all the APs are allowed to transmit simultaneously due to the contention-based nature of carrier sense multiple access with collision avoidance (CSMA/CA) channel accessing scheme [16]. Te probability that the network ofers a data rate to the user which is greater than a threshold ρ r can be defned as…”

Towards Enabling Multihop Wireless Local Area Networks for Disaster Communications

“…In the two-hop relaying link, the physical channel between the source and the relay is called the relay link, whereas between the relay and the destination it is called the access link. However, the relay node is used between the source node and the destination node in the multihop WLAN system to support transmission [11]. Multihop networks are deployed in seminars, campuses, exhibitions, and libraries to increase the coverage area.…”

Large Deviation Principle for Empirical SINR Measure of Critical Telecommunication Networks

“…See, example Aravanis, Lam, Muoz, Pascual-Iserte and Di Renzo [3]. Marvi, Aijaz and Khurram [17] posited that 8.3 billion hand-held devices and 3.3 billion machine-to-machine (M2M) devices will be connected by 2021. The number of connected devices would clearly exceed the expected global population of 7.8 billion by that time.…”