Proportional Fair RAT Aggregation in HetNets

Heterogeneity in wireless network architectures (i.e., the coexistence of 3G, LTE, 5G, WiFi, etc.) has become a key component of current and future generation cellular networks. Simultaneous aggregation of each client's traffic across multiple such radio access technologies (RATs) / base stations (BSs) can significantly increase the system throughput, and has become an important feature of cellular standards on multi-RAT integration. Distributed algorithms that can realize the full potential of this aggregation are thus of great importance to operators. In this paper, we study the problem of resource allocation for multi-RAT traffic aggregation in HetNets (heterogeneous networks). Our goal is to ensure that the resources at each BS are allocated so that the aggregate throughput achieved by each client across its RATs satisfies a proportional fairness (PF) criterion. In particular, we provide a simple distributed algorithm for resource allocation at each BS that extends the PF allocation algorithm for a single BS. Despite its simplicity and lack of coordination across the BSs, we show that our algorithm converges to the desired PF solution and provide (tight) bounds on its convergence speed. We also study the characteristics of the optimal solution and use its properties to prove the optimality of our algorithm's outcomes.Comment: Extended version of the 31st International Teletraffic Congress (ITC 2019) conference pape

Gated Recurrent Units for Blockage Mitigation in mmWave Wireless

Millimeter-Wave (mmWave) communication is susceptible to blockages, which can significantly reduce the signal strength at the receiver. Mitigating the negative impacts of blockages is a key requirement to ensure reliable and high throughput mmWave communication links. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. In this paper, we address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To address the problem, we developed a Gated Recurrent Unit (GRU) model that is trained using periodically exchanged messages in mmWave systems. We gathered extensive amount of simulation data from a commercially available mmWave simulator, show that the proposed method does not incur any additional communication overhead, and that it achieves outstanding results in selecting the optimal blockage mitigation method with an accuracy higher than 93%. We also show that the proposed method significantly increases the amount of transferred data compared to several other blockage mitigation policies

Bounds for the capacity of wireless multihop networks imposed by topology and demand

Existing work on the capacity of wireless networks predominantly considers homogeneous random networks with random work load. The most relevant bounds on the network capacity, e.g., take into account only the number of nodes and the area of the network. However, these bounds can significantly overestimate the achievable capacity in real world situations where network topology or traffic patterns often deviate from these simplistic assumptions. To provide analytically tractable yet asymptotically tight approximations of network capacity we propose a novel space-based approach. At the heart of our methodology lie simple functions which indicate the presence of active transmissions near any given location in the network and which constitute a tool well suited to untangle the interactions of simultaneous transmissions. We are able to provide capacity bounds which are tighter than the traditional ones and which involve topology and traffic patterns explicitly, e.g., through the length of Euclidean Minimum Spanning Tree, or through traffic demands between clusters of nodes. As an additional novelty our results cover unicast, multicast and broadcast and are asymptotically tight. Notably, our capacity bounds are simple enough to require only knowledge of node location, and there is no need for solving or optimizing multi-variable equations in our approach

Arena function ::a framework for computing capacity bounds in wireless networks

Bounds on the capacity of wireless networks often rely on simplifying assumptions and are given in terms of coarse network parameters such as the number of nodes. While useful due to their simplicity such bounds can significantly overestimate the achievable capacity in real world situations, ignoring actual network topology and traffic patterns. The results of this paper improve such analytical results on network capacity in several ways. At the heart of our methodology lies the concept of transmission arenas which indicate the presence of active transmissions near any given location in the network. This novel space-based approach is well suited to untangle the interactions of simultaneous transmissions. Avoiding a graph-based model of the network it opens new avenues of studying capacities. For homogeneous networks we recover classical bounds. However, our methodology applies to arbitrary networks and can, thus, inform placing and activating of nodes also in the presence of clustering. Our method works with all classical channel models and dimensions. It provides bounds on the transport capacity which involve only high level knowledge of node locations, such as the length of Euclidean Minimum Spanning Tree. As an additional novelty we establish bounds on wireless unicast and multicast capacities