Providing connectivity to around half of the world population living in rural or underprivileged areas is a tremendous challenge, but, at the same time, a unique opportunity. Access to the Internet would provide the population living in these areas a possibility to progress on the educational, health, environment, and business levels. In this article, a survey of technologies for providing connectivity to rural areas, which can help address this challenge, is provided. Although access/fronthaul and backhaul techniques are discussed in this article, it is noted that the major limitation for providing connectivity to rural and underprivileged areas is the cost of backhaul deployment. In addition, energy requirements and cost-efficiency of the studied technologies are analyzed. In fact, the challenges faced for deploying an electricity network, as a prerequisite for deploying communication networks, are huge in these areas, and they are granted an important share of the discussions in this article. Furthermore, typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. The main initiatives by key international players aiming to provide rural connectivity are also described. Moreover, directions for the future evolution of rural connectivity are outlined in this article. Although there is no single solution that can solve all rural connectivity problems, building gradually on the current achievements in order to reach ubiquitous connectivity, while taking into account the particularities of each region and tailoring the solution accordingly, seems to be the most suitable path to follow.

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A Key 6G Challenge and Opportunity—Connecting the Base of the Pyramid: A Survey on Rural Connectivity

The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and optimized routing will soon become essential components of the IoT wireless communication paradigm. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters ( e.g. , frequency band, symbol modulation, coding rate, route selection, etc.) to reach the network’s optimal operating point. Given that the majority of the IoT will be composed of tiny, mobile, and energy-constrained devices, traditional techniques based on a priori network optimization may not be suitable, since (i) an accurate model of the environment may not be readily available in practical scenarios; (ii) the computational requirements of traditional optimization techniques may prove unbearable for IoT devices. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning’s popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms. This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions of machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Additionally, before concluding, we also provide a brief discussion of the application of machine learning in IoT beyond wireless communication. Finally, each of these discussions is accompanied by a detailed analysis of the related open problems and challenges.

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

Cognitive radio for M2M and Internet of Things: A survey

Time-critical and delay-sensitive multimedia applications require more spectrum and transmission resources. With the provision of cognitive radios (CRs), the underutilized spectrum resources can be exploited to gain more bandwidth for the bandwidth hungry applications (multimedia applications). Cognitive radio networks (CRNs) also have the flexibility to adjust their transmission parameters according to the needs of multimedia services or applications. For this reason, wireless multimedia cognitive radio networks (WMCRNs) have gained much attentions in today’s research domain. In this paper, we present a comprehensive survey of WMCRNs. Various multimedia applications supported by CRNs, and various CR-based wireless networks are surveyed. We highlight the routing and link layer protocols used for WMCRNs. We cover the quality-of-experience design and security requirements for transmitting multimedia content over CRNs. We provide an in-depth study of white space, television white space, and cross-layer designs that have been used for WMCRNs. We also survey the major spectrum sensing approaches used for the communications of bandwidth hungry and time-critical data over CRNs.

Wireless Multimedia Cognitive Radio Networks: A Comprehensive Survey

With the evolution of network function virtualization (NFV), diverse network services can be flexibly offered as service function chains (SFCs) consisted of different virtual network functions (VNFs). However, network state and traffic typically exhibit unpredictable variations due to stochastically arriving requests with different quality of service (QoS) requirements. Thus, an adaptive online SFC deployment approach is needed to handle the real-time network variations and various service requests. In this paper, we firstly introduce a Markov decision process (MDP) model to capture the dynamic network state transitions. In order to jointly minimize the operation cost of NFV providers and maximize the total throughput of requests, we propose NFVdeep, an adaptive, online, deep reinforcement learning approach to automatically deploy SFCs for requests with different QoS requirements. Specifically, we use a serialization-and-backtracking method to effectively deal with large discrete action space. We also adopt a policy gradient based method to improve the training efficiency and convergence to optimality. Extensive experimental results demonstrate that NFVdeep converges fast in the training process and responds rapidly to arriving requests especially in large, frequently transferred network state space. Consequently, NFVdeep surpasses the state-of-the-art methods by 32.59% higher accepted throughput and 33.29% lower operation cost on average.

NFVdeep: adaptive online service function chain deployment with deep reinforcement learning

Thanks to network slicing, 5G networks will support a variety of services in a flexible and swift manner. In this context, we seek to make high-quality, joint optimal decisions concerning the placement of VNFs across the physical hosts for realizing the services, and the allocation of CPU resources in VNFs sharing a host. To this end, we present a queuing-based system model, accounting for all the entities involved in 5G networks. Then, we propose a fast and efficient solution strategy yielding near-optimal decisions. We evaluate our approach in multiple scenarios that well represent real-world services, and find it to consistently outperform state-of-the-art alternatives and closely match the optimum.

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Joint VNF Placement and CPU Allocation in 5G

One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly , accounting for their mutual interaction, efficiently. To this end, we formulate a queuing-based model and use it at the network orchestrator to optimally match the vertical’s requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out an accounting for multiple scenarios representing the real-world services, shows that MaxZ performs substantially better than the state-of-the-art alternatives and consistently close to the optimum.

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VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

In this paper, we propose a class of energy-efficient dynamic spectrum access (DSA) protocols for secondary user (SU) communication over a single primary user (PU) channel. The proposed variants of DSA can be optimized with respect to different back-off strategies and SU packet lengths. Via Markov chain models and numerical analysis, we derive the optimal SU packet length and inter-sensing time for optimal SU performance in the DSA variants. We evaluate the protocol performance in terms of SU goodput, SU energy efficiency, and PU collision ratio. Adaptability of the proposed SU operation protocol in practical scenarios is tested over cellular GSM band as well as under real-time video over IP based PU traffic in ISM band. Our performance studies demonstrate that the proposed protocols offer significantly high channel utilization while keeping the PU collisions below an acceptable threshold. The proposed protocol operation is also outlined, where the protocol adapts to the changing PU traffic load for optimized performance.

eDSA: Energy-Efficient Dynamic Spectrum Access Protocols for Cognitive Radio Networks

One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, and efficiently. To this end, we formulate a queuing-based model and use it at the network orchestrator to optimally match the vertical's requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out accounting for multiple scenarios representative of real-world services, shows that MaxZ performs substantially better than state-of-the-art alternatives and consistently close to the optimum.

/pdf/vnf-placement-and-resource-allocation-for-the-support-of-xw1958syhi.pdf

In this paper, we present a quality-of-service (QoS)-aware cooperative downlink scheduling approach for cell-edge and handoff users that offers more reliability and higher effective capacity. The cooperation (handoff) region is defined for active handoff users between two adjacent base stations (BSs) as a function of the user QoS requirements and network load. In addition, the proposed technique inherently acquires intercell interference (ICI) coordination by adjusting the position and size of the cooperation window suitably. Numerical results are presented showing reliability, user QoS and capacity gain performance, and the region for cooperative scheduling in a coded communication scenario. Our analysis indicates that cooperation provides relatively less gain in effective capacity, i.e., up to about 40% with respect to noncooperative handoff, when the QoS requirement is loose. On the other hand, when the QoS requirement is more stringent, the effective capacity gain can increase up to nearly 100%. Additionally, we show that, while for applications with a loose QoS requirement the cooperation window size is small, i.e., nearly 1% of the total area of the BSs participating in cooperation, it increases quite significantly, i.e., up to nearly 25%, for the applications with stringent QoS. Although the outage performance of the proposed approach is poorer than the joint transmission mode of a cooperative multipoint scheme in lightly loaded networks, its effective capacity is significantly higher under varying network traffic load and QoS constraints.

https://web.iitd.ac.in/~swadesd/pubs/JNL/Handoff-TVT2015.pdf

Satyam Agarwal

Papers

Joint VNF Placement and CPU Allocation in 5G

VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

eDSA: Energy-Efficient Dynamic Spectrum Access Protocols for Cognitive Radio Networks

VNF Placement and Resource Allocation for the Support of Vertical Services in 5G Networks

QoS-Aware Downlink Cooperation for Cell-Edge and Handoff Users