Ambient RF Energy Harvesting in Ultra-Dense Small Cell Networks: Performance and Trade-offs

In order to minimize electric grid power consumption, energy harvesting from ambient RF sources is considered as a promising technique for wireless charging of low-power devices. To illustrate the design considerations of RF-based ambient energy harvesting networks, this article first points out the primary challenges of implementing and operating such networks, including non-deterministic energy arrival patterns, energy harvesting mode selection, energy-aware cooperation among base stations (BSs), etc. A brief overview of the recent advancements and a summary of their shortcomings are then provided to highlight existing research gaps and possible future research directions. To this end, we investigate the feasibility of implementing RF-based ambient energy harvesting in ultra-dense small cell networks (SCNs) and examine the related trade-offs in terms of the energy efficiency and signal-to-interference-plus-noise ratio (SINR) outage probability of a typical user in the downlink. Numerical results demonstrate the significance of deploying a mixture of on-grid small base stations (SBSs)~(powered by electric grid) and off-grid SBSs~(powered by energy harvesting) and optimizing their corresponding proportions as a function of the intensity of active SBSs in the network.Comment: IEEE Wireless Communications, to appea

Optimal Hierarchical Radio Resource Management for HetNets with Flexible Backhaul

Providing backhaul connectivity for macro and pico base stations (BSs) constitutes a significant share of infrastructure costs in future heterogeneous networks (HetNets). To address this issue, the emerging idea of flexible backhaul is proposed. Under this architecture, not all the pico BSs are connected to the backhaul, resulting in a significant reduction in the infrastructure costs. In this regard, pico BSs without backhaul connectivity need to communicate with their nearby BSs in order to have indirect accessibility to the backhaul. This makes the radio resource management (RRM) in such networks more complex and challenging. In this paper, we address the problem of cross-layer RRM in HetNets with flexible backhaul. We formulate this problem as a two-timescale non-convex stochastic optimization which jointly optimizes flow control, routing, interference mitigation and link scheduling in order to maximize a generic network utility. By exploiting a hidden convexity of this non-convex problem, we propose an iterative algorithm which converges to the global optimal solution. The proposed algorithm benefits from low complexity and low signalling, which makes it scalable. Moreover, due to the proposed two-timescale design, it is robust to the backhaul signalling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines.Comment: We note that the definition of Subproblem 2 (named as Problem P2 in the paper) was missed in the published version of this paper in IEEE Transactions on Wireless Communications. This definition is denoted by equation (8) in this arXiv versio

End-to-End Simulation of 5G mmWave Networks

Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns--3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and highly customizable, making it easy to integrate algorithms or compare Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example. The module is interfaced with the core network of the ns--3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.Comment: 25 pages, 16 figures, submitted to IEEE Communications Surveys and Tutorials (revised Jan. 2018

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure

Small Cell Deployments: Recent Advances and Research Challenges

This paper summarizes the outcomes of the 5th International Workshop on Femtocells held at King's College London, UK, on the 13th and 14th of February, 2012.The workshop hosted cutting-edge presentations about the latest advances and research challenges in small cell roll-outs and heterogeneous cellular networks. This paper provides some cutting edge information on the developments of Self-Organizing Networks (SON) for small cell deployments, as well as related standardization supports on issues such as carrier aggregation (CA), Multiple-Input-Multiple-Output (MIMO) techniques, and enhanced Inter-Cell Interference Coordination (eICIC), etc. Furthermore, some recent efforts on issues such as energy-saving as well as Machine Learning (ML) techniques on resource allocation and multi-cell cooperation are described. Finally, current developments on simulation tools and small cell deployment scenarios are presented. These topics collectively represent the current trends in small cell deployments.Comment: 19 pages, 22 figure

Power Allocation for Massive MIMO-based, Fronthaul-constrained Cloud RAN Systems

Cloud radio access network (C-RAN) and massive multiple-input-multiple-output (MIMO) are two key enabling technologies to meet the diverse and stringent requirements of the 5G use cases. In a C-RAN system with massive MIMO, fronthaul is often the bottleneck due to its finite capacity and transmit precoding is moved to the remote radio head to reduce the capacity requirements on fronthaul. For such a system, we optimize the power allocated to the users to maximize first the weighted sum rate and then the energy efficiency (EE) while explicitly incorporating the capacity constraints on fronthaul. We consider two different fronthaul constraints, which model capacity constraints on different parts of the fronthaul network. We develop successive convex approximation algorithms that achieve a stationary point of these non-convex problems. To this end, we first present novel, locally tight bounds for the user rate expression. They are used to obtain convex approximations of the original non-convex problems, which are then solved by solving their dual problems. In EE maximization, we also employ the Dinkelbach algorithm to handle the fractional form of the objective function. Numerical results show that the proposed algorithms significantly improve the network performance compared to a case with no power control and achieves a better performance than an existing algorithm

An Auction Approach to Distributed Power Allocation for Multiuser Cooperative Networks

This paper studies a wireless network where multiple users cooperate with each other to improve the overall network performance. Our goal is to design an optimal distributed power allocation algorithm that enables user cooperation, in particular, to guide each user on the decision of transmission mode selection and relay selection. Our algorithm has the nice interpretation of an auction mechanism with multiple auctioneers and multiple bidders. Specifically, in our proposed framework, each user acts as both an auctioneer (seller) and a bidder (buyer). Each auctioneer determines its trading price and allocates power to bidders, and each bidder chooses the demand from each auctioneer. By following the proposed distributed algorithm, each user determines how much power to reserve for its own transmission, how much power to purchase from other users, and how much power to contribute for relaying the signals of others. We derive the optimal bidding and pricing strategies that maximize the weighted sum rates of the users. Extensive simulations are carried out to verify our proposed approach.Comment: Accepted by IEEE Transactions on Wireless Communication

A Survey on QoE-oriented Wireless Resources Scheduling

Future wireless systems are expected to provide a wide range of services to more and more users. Advanced scheduling strategies thus arise not only to perform efficient radio resource management, but also to provide fairness among the users. On the other hand, the users' perceived quality, i.e., Quality of Experience (QoE), is becoming one of the main drivers within the schedulers design. In this context, this paper starts by providing a comprehension of what is QoE and an overview of the evolution of wireless scheduling techniques. Afterwards, a survey on the most recent QoE-based scheduling strategies for wireless systems is presented, highlighting the application/service of the different approaches reported in the literature, as well as the parameters that were taken into account for QoE optimization. Therefore, this paper aims at helping readers interested in learning the basic concepts of QoE-oriented wireless resources scheduling, as well as getting in touch with its current research frontier.Comment: Revised version: updated according to the most recent related literature; added references; corrected typo

Mechanism Design for Base Station Association and Resource Allocation in Downlink OFDMA Network

We consider a resource management problem in a multi-cell downlink OFDMA network, whereby the goal is to find the optimal per base station resource allocation and user-base station assignment. The users are assumed to be strategic/selfish who have private information on downlink channel states and noise levels. To induce truthfulness among the users as well as to enhance the spectrum efficiency, the resource management strategy needs to be both incentive compatible and efficient. However, due to the mixed (discrete and continuous) nature of resource management in this context, the implementation of any incentive compatible mechanism that maximizes the system throughput is NP-hard. We consider the dominant strategy implementation of an approximately optimal resource management scheme via a computationally tractable mechanism. The proposed mechanism is decentralized and dynamic. More importantly, it ensures the truthfulness of the users and it implements a resource allocation solution that yields at least 1/2 of the optimal throughput. Simulations are provided to illustrate the effectiveness of the performance of the proposed mechanism.Comment: Technical Report, contains the proof of NP-hardness which is omitted in the Journal versio

Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning

The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems