The exponential growth and availability of data in all forms is the main booster to the continuing evolution in the communications industry. The popularization of traffic-intensive applications including high definition video, 3-D visualization, augmented reality, wearable devices, and cloud computing defines a new era of mobile communications. The immense amount of traffic generated by today’s customers requires a paradigm shift in all aspects of mobile networks. Ultradense network (UDN) is one of the leading ideas in this racetrack. In UDNs, the access nodes and/or the number of communication links per unit area are densified. In this paper, we provide a survey-style introduction to dense small cell networks. Moreover, we summarize and compare some of the recent achievements and research findings. We discuss the modeling techniques and the performance metrics widely used to model problems in UDN. Also, we present the enabling technologies for network densification in order to understand the state-of-the-art. We consider many research directions in this survey, namely, user association, interference management, energy efficiency, spectrum sharing, resource management, scheduling, backhauling, propagation modeling, and the economics of UDN deployment. Finally, we discuss the challenges and open problems to the researchers in the field or newcomers who aim to conduct research in this interesting and active area of research.

Ultra-Dense Networks: A Survey

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unlicensed users (i.e., secondary users, SUs) to access the licensed spectrum. Market-driven spectrum trading is an efficient way to achieve dynamic spectrum accessing/sharing. In this paper, we consider the problem of spectrum trading with single primary spectrum owner (or primary user, PO) selling his idle spectrum to multiple SUs. We model the trading process as a monopoly market, in which the PO acts as monopolist who sets the qualities and prices for the spectrum he sells, and the SUs act as consumers who choose the spectrum with appropriate quality and price for purchasing. We design a monopolist-dominated quality-price contract, which is offered by the PO and contains a set of quality-price combinations each intended for a consumer type. A contract is feasible if it is incentive compatible (IC) and individually rational (IR) for each SU to purchase the spectrum with the quality-price intended for his type. We propose the necessary and sufficient conditions for the contract to be feasible. We further derive the optimal contract, which is feasible and maximizes the utility of the PO, for both discrete-consumer-type model and continuous-consumer-type model. Moreover, we analyze the social surplus, i.e., the aggregate utility of both PO and SUs, and we find that, depending on the distribution of consumer types, the social surplus under the optimal contract may be less than or close to the maximum social surplus.

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Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

This work studies the joint design of precoding and backhaul compression strategies for the downlink of cloud radio access networks. In these systems, a central encoder is connected to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art approaches, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals of different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate with respect to both the precoding matrix and the joint correlation matrix of the quantization noises is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. Moreover, in order to enable the practical implementation of multivariate compression across BSs, a novel architecture is proposed based on successive steps of minimum mean-squared error (MMSE) estimation and per-BS compression. Robust design with respect to imperfect channel state information is also discussed. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on the separate design of precoding and compression or independent compression across the BSs.

Joint Precoding and Multivariate Backhaul Compression for the Downlink of Cloud Radio Access Networks

The Long Term Evolution Advanced (LTE-A) network, is a heterogeneous network, where macro and pico base stations (BSs) coexist to improve spectral efficiency per unit area. Systems and methods described herein attempt to provide a solution to the interference coordination problem between macro BSs and pico user equipments (UEs). Specifically, the systems and methods conduct interference coordination based on the concept of almost blank subframe (ABS), which is supported by the LTE-A standard. The macro BSs choose their ABS configurations in a cooperative way such that the overall system throughput is optimized.

ABS-based Method for Inter Cell Interference Coordination in LTE-Advanced Networks

We consider a cognitive radio system like the future 80222 networks where license-exempt service providers (SPs) will share a fixed spectrum in a non-interference basis to each other and also to the licensed users in that spectrum The percentage of spectrum utilized by one SP depends on how many users it is serving and how much spectrum each user application demands We assume that an user can obtain service from all the SPs The quality of service depends on system parameters such as number of users and SPs, the channel conditions between the users and SPs and the total power available at each user We adopt an user utility maximization framework to analyze this system Given the user utility functions, and the above mentioned system parameters we derive optimal values of spectrum that the users should obtain from the SPs We also introduce the notion of spectrum price and use it to demonstrate several key results about spectrum allocation The spectrum price proves to be the regulatory mechanism that brings about coordination amongst the SPs with minimal control messaging Our approach thus strikes a balance between a centralized network and a fully uncoordinated open access network

A Framework for Dynamic Spectrum Sharing Between Cognitive Radios

Dynamic allocation of spectrum prior to transmission is an important feature for next generation wireless networks. In this work, we develop and analyze a model for dynamic spectrum allocation, that is applicable for a broad class of practical systems. We consider multiple service providers (SPs), in the same geographic region, that share a fixed spectrum, on a non-interference basis. This spectrum is allocated to their customer end users for transmission to the SPs. Assuming that a user can obtain service from all the SPs, this work develops an efficient algorithm for spectrum allocation. The quality of service depends on system parameters such as number of users and SPs, the channel conditions between the users and SPs and the total transmit power of each user. The SPs have different efficiencies of reception. We adopt a user utility maximization framework to analyze this system. We develop the notion of spectrum price that enables a simple distributed spectrum allocation with minimal coordination among the SPs and users. Given the user utility functions and the system parameters, we characterize the spectrum price and the users' optimal bandwidth allocations. Our work provides theoretical bounds on performance limits of practical operator to user based dynamic spectrum allocation systems and also gives insights to actual system design.

Dynamic spectrum allocation for uplink users with heterogeneous utilities

A central base station for interference management in a distributed antenna system comprises a processor; a memory; and a frequency partition module configured to divide the remote radio heads into clusters and allocate a frequency band to each cluster. Multiple clusters use the same frequency band and groups of clusters are formed. A joint scheduler is configured, for each group, to activate at most two clusters at any given time and deactivate remaining clusters in the group, one of the at most two activated clusters being a serving cluster of the group, and to associate one or more users with the serving cluster in the group. An interference alignment module is configured to apply downlink interference alignment between the at most two activated clusters in each group to align transmit directions of all interferences between the at most two activated clusters.

Interference management for distributed antenna systems

A comprehensive summary of theoretical and practical developments in LTE Heterogeneous NetworksThe last decade has witnessed the proliferation of mobile broadband data and the trend is likely to increase in the coming years. Current cellular networks are ill equipped to deal with this surge in demand. To satisfy user demand and maximize profits, a new paradigm to operate networks is needed. Heterogeneous networks, that deploy an overlay of small cells with limited coverage and transmit power, over a macro coverage area is the solution by providing capacity and coverage where it is needed.This book presents a comprehensive overview of small cell based heterogeneous networks within the framework of 3GPP LTE-Advanced which is the major enabler of current and future heterogeneous networks. The book first establishes the basics of LTE standards 8 -10. Wherever relevant, the underlying theory of wireless communications is explained and the signaling and protocol aspects of LTE Releases 8-10 are presented. Next the book presents a systematic study of the inter cell interference (eICIC and FeICIC) mechanisms that have been standardized in LTE releases 10 and 11 to mitigate the interference arising in heterogeneous networks. From simple blank subframe design and implementation, the book discusses more advanced transceiver signal processing and carrier aggregation (CA) based mechanisms to improve performance. Besides data, control channel enhancements such as enhanced PDCCH (ePDCCH) are also discussed.Subsequently the book discusses the possibility of base stations being allowed to coordinate to manage interference. This technique, called CoMP, has the potential of vastly improving network performance. However several practical challenges first have to be overcome before this potential can be realized. The book presents the different CoMP categories introduced in LTE release 11, the required signal processing and the changes that were introduced in Release-11 for supporting CoMP. The book then presents the state of the art developments in heterogeneous networks that are currently taking place in 3GPP with the initiation of Release 12. A whole array of new technologies have been introduced such as dynamic switching of small cells, new carrier types with reduced control signaling, dynamic reconfiguration of TDD-LTE, joint configuration of TDD and FDD via carrier aggregation and lastly advanced MIMO signal processing with three dimensional beamforming. All these technologies will work in unison leading to efficient operations of small cells.The authors thus comprehensively summarize the advances in heterogeneous networks over the last couple of years as reflected in various LTE releases and then look ahead at what to expect in the future. Fully illustrated throughout and with an accompanying website including Matlab code for simulating heterogeneous networks, LTE channel models, and References to 3GPP specifications, contributions, and updates on recent standardization activities. The authors, being involved in LTE standardization, are well placed to give an excellent view on this topic, including valuable background and design rationale.A comprehensive summary of wireless communications theory and practical developments in LTE heterogeneous networks.Authors are experts in this field and are active members in standardization proceedings, enabling up-to-date coverage of current developmentsMultiple case studies explain network design optimization of various heterogeneous network deployments.Accompanying website includes Matlab code for simulating heterogeneous networks, LTE channel models, and References to 3GPP specifications, contributions, and updates on recent standardization activitiesEssential reading for Engineers and practitioners in wireless industry.

Joydeep Acharya

Papers

ABS-based Method for Inter Cell Interference Coordination in LTE-Advanced Networks

A Framework for Dynamic Spectrum Sharing Between Cognitive Radios

Dynamic spectrum allocation for uplink users with heterogeneous utilities

Interference management for distributed antenna systems

Heterogeneous Networks in LTE-Advanced