This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

Pushing data traffic from cellular to WiFi is an example of inter radio access technology (RAT) offloading. While this clearly alleviates congestion on the over-loaded cellular network, the ultimate potential of such offloading and its effect on overall system performance is not well understood. To address this, we develop a general and tractable model that consists of M different RATs, each deploying up to K different tiers of access points (APs), where each tier differs in transmit power, path loss exponent, deployment density and bandwidth. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. The distribution of rate over the entire network is then derived for a weighted association strategy, where such weights can be tuned to optimize a particular objective. We show that the optimum fraction of traffic offloaded to maximize SINR coverage is not in general the same as the one that maximizes rate coverage, defined as the fraction of users achieving a given rate.

/pdf/offloading-in-heterogeneous-networks-modeling-analysis-and-3glpsagfmf.pdf

Offloading in Heterogeneous Networks: Modeling, Analysis, and Design Insights

The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this article, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this article is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.

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A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

This chapter contains sections titled: Product Definition for Broadband Wireless Systems Technology Drivers Evolving Wireless Broadband Market Segments Open Systems and Intelligence at the Edge Radio Network System Engineering References

Always Best Connected

When several radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a semi-Markov decision process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization.

/pdf/a-network-assisted-approach-for-rat-selection-in-16n23bdqb3.pdf

A Network-Assisted Approach for RAT Selection in Heterogeneous Cellular Networks

LoRaWAN is a media access control (MAC) protocol for wide area networks. It is designed to allow low-powered devices to communicate with Internet-connected applications over long-range wireless connections. The targeted dense deployment will inevitably cause a shortage of radio resources. Hence, autonomous and lightweight radio resource management is crucial to offer ultra-long battery lifetime for LoRa devices. One of the most promising solutions to such a challenge is the use of artificial intelligence. This will enable LoRa devices to use innovative and inherently distributed learning techniques, thus freeing them from draining their limited energy by constantly communicating with a centralized controller.Before proceeding with the deployment of self-managing solutions on top of a LoRaWAN application, it is sensible to conduct simulation-based studies to optimize the design of learning-based algorithms as well as the application under consideration. Unfortunately, a network simulator for such a context is not fully considered or lacks real deployment parameters. In order to address this shortcoming, we have developed an event-based simulator for resource allocation in LoRaWAN. To demonstrate the usefulness of our simulator, extensive simulations were run in a realistic environment taking into account physical phenomenon in LoRaWAN such as the capture effect and inter-spreading factor interference. The simulation results show that the proposed simulator provides a flexible and efficient environment to evaluate various network design parameters and self-management solutions as well as verify the effectiveness of distributed reinforcement-based learning algorithms for resource allocation problems in LoRaWAN.

/pdf/lora-mab-a-flexible-simulator-for-decentralized-learning-zk7l5545b8.pdf

LoRa-MAB: A Flexible Simulator for Decentralized Learning Resource Allocation in IoT Networks

In heterogeneous wireless networks, different radio access technologies are integrated and may be jointly managed. To optimize composite network performance and capacity, Common Radio Resource Management (CRRM) mechanisms need to be defined. This paper tackles the access technology selection -- a key CRRM functionality -- and proposes a hybrid decision framework to dynamically integrate operator objectives and user preferences. Mobile users make their selection decision based on their needs and preferences as well as on the cost and QoS information signaled by the network. Appropriate decisional information should then be derived so that the network better utilizes its radio resources, while mobile users maximize their own utility. We thus present two tuning policies, namely the staircase and the slope tuning policies, to dynamically modulate this information. Simulation results illustrate the gain from using our tuning policies in comparison with a static one: they lead to better network performance, larger operator gain and higher user satisfaction.

https://hal.archives-ouvertes.fr/hal-01018232/document

A Hybrid Approach for Radio Access Technology Selection in Heterogeneous Wireless Networks

Nowadays networks are characterized by the abundance of diverse Radio Access Interfaces (RAI) in the same operating area. The various wireless interfaces can belong to the same Radio Access Technology or not. In such a scenario, a mobile user will be able to select selfishly one of the available radio interfaces in order to enhance its own performance. Therefore, the radio access selection policy is vital and must be designed astutely to avoid resource wastage. In this paper, the RAI selection process is apprehended as a congestion game which is a class of noncooperative games in which users share a common set of limited resources. The cost sustained by a given user depends upon the congestion impact inflected by other users sharing the same resource. Devising distributed resource sharing schemes that optimize user air interface selection depends crucially on the existence of Nash equilibria for the modelling congestion games. In this paper, we model the downlink access for three main broadband technologies (WiMAX, WiFi and 3.5G HSDPA) and study the existence of pure Nash equilibria for various multi-RAI scenarios involving those technologies.

Congestion Games for Distributed Radio Access Selection in Broadband Networks

Nowadays, with the abundance of diverse air interfaces in the same operating area, advanced Radio Resource Management (RRM) is vital to take advantage of the available system resources. In such a scenario, a mobile user will be able to connect concurrently to different wireless access networks. In this paper, we consider the downlink of a hybrid network with two broadband Radio Access Technologies (RAT): WiMAX and WiFi. Two approaches are proposed to load balance the traffic of every user between the two available RATs: an individual approach where mobile users selfishly strive to improve their performance and a global approach where resource allocation is made in a way to satisfy all mobile users. We devise for the individual approach a fully distributed resource management scheme portrayed as a non-cooperative game. We characterize the Nash equilibriums of the proposed RRM game and put forward a decentralized algorithm based on replicator dynamics to achieve those equilibriums. In the global approach, resources are assigned by the system in order to enhance global performances. For the two approaches, we show that after convergence, each user is connected to a single RAT which avoids costly traffic splitting between available RATs.

/pdf/individual-vs-global-radio-resource-management-in-a-hybrid-fb25hxgyn1.pdf

Kinda Khawam

Papers

A Network-Assisted Approach for RAT Selection in Heterogeneous Cellular Networks

LoRa-MAB: A Flexible Simulator for Decentralized Learning Resource Allocation in IoT Networks

A Hybrid Approach for Radio Access Technology Selection in Heterogeneous Wireless Networks

Congestion Games for Distributed Radio Access Selection in Broadband Networks

Individual vs. Global Radio Resource Management in a Hybrid Broadband Network