Principles of Physical Layer Security in Multiuser Wireless Networks: A Survey

This paper provides a comprehensive review of the domain of physical layer security in multiuser wireless networks. The essential premise of physical-layer security is to enable the exchange of confidential messages over a wireless medium in the presence of unauthorized eavesdroppers without relying on higher-layer encryption. This can be achieved primarily in two ways: without the need for a secret key by intelligently designing transmit coding strategies, or by exploiting the wireless communication medium to develop secret keys over public channels. The survey begins with an overview of the foundations dating back to the pioneering work of Shannon and Wyner on information-theoretic security. We then describe the evolution of secure transmission strategies from point-to-point channels to multiple-antenna systems, followed by generalizations to multiuser broadcast, multiple-access, interference, and relay networks. Secret-key generation and establishment protocols based on physical layer mechanisms are subsequently covered. Approaches for secrecy based on channel coding design are then examined, along with a description of inter-disciplinary approaches based on game theory and stochastic geometry. The associated problem of physical-layer message authentication is also introduced briefly. The survey concludes with observations on potential research directions in this area.Comment: 23 pages, 10 figures, 303 refs. arXiv admin note: text overlap with arXiv:1303.1609 by other authors. IEEE Communications Surveys and Tutorials, 201

Resource Allocation for Multiple-Input and Multiple-Output Interference Networks

To meet the exponentially increasing traffic data driven by the rapidly growing mobile subscriptions, both industry and academia are exploring the potential of a new genera- tion (5G) of wireless technologies. An important 5G goal is to achieve high data rate. Small cells with spectrum sharing and multiple-input multiple-output (MIMO) techniques are one of the most promising 5G technologies, since it enables to increase the aggregate data rate by improving the spectral efficiency, nodes density and transmission bandwidth, respectively. However, the increased interference in the densified networks will in return limit the achievable rate performance if not properly managed. The considered setup can be modeled as MIMO interference networks, which can be classified into the K-user MIMO interference channel (IC) and the K-cell MIMO interfering broadcast channel/multiple access channel (MIMO-IBC/IMAC) according to the number of mobile stations (MSs) simultaneously served by each base station (BS). The thesis considers two physical layer (PHY) resource allocation problems that deal with the interference for both models: 1) Pareto boundary computation for the achiev- able rate region in a K-user single-stream MIMO IC and 2) grouping-based interference alignment (GIA) with optimized IA-Cell assignment in a MIMO-IMAC under limited feedback. In each problem, the thesis seeks to provide a deeper understanding of the system and novel mathematical results, along with supporting numerical examples. Some of the main contributions can be summarized as follows. It is an open problem to compute the Pareto boundary of the achievable rate region for a K-user single-stream MIMO IC. The K-user single-stream MIMO IC models multiple transmitter-receiver pairs which operate over the same spectrum simultaneously. Each transmitter and each receiver is equipped with multiple antennas, and a single desired data stream is communicated in each transmitter-receiver link. The individual achievable rates of the K users form a K-dimensional achievable rate region. To find efficient operating points in the achievable rate region, the Pareto boundary computation problem, which can be formulated as a multi-objective optimization problem, needs to be solved. The thesis transforms the multi-objective optimization problem to two single-objective optimization problems–single constraint rate maximization problem and alternating rate profile optimization problem, based on the formulations of the ε-constraint optimization and the weighted Chebyshev optimization, respectively. The thesis proposes two alternating optimization algorithms to solve both single-objective optimization problems. The convergence of both algorithms is guaranteed. Also, a heuristic initialization scheme is provided for each algorithm to achieve a high-quality solution. By varying the weights in each single-objective optimization problem, numerical results show that both algorithms provide an inner bound very close to the Pareto boundary. Furthermore, the thesis also computes some key points exactly on the Pareto boundary in closed-form. A framework for interference alignment (IA) under limited feedback is proposed for a MIMO-IMAC. The MIMO-IMAC well matches the uplink scenario in cellular system, where multiple cells share their spectrum and operate simultaneously. In each cell, a BS receives the desired signals from multiple MSs within its own cell and each BS and each MS is equipped with multi-antenna. By allowing the inter-cell coordination, the thesis develops a distributed IA framework under limited feedback from three aspects: the GIA, the IA-Cell assignment and dynamic feedback bit allocation (DBA), respec- tively. Firstly, the thesis provides a complete study along with some new improvements of the GIA, which enables to compute the exact IA precoders in closed-form, based on local channel state information at the receiver (CSIR). Secondly, the concept of IA-Cell assignment is introduced and its effect on the achievable rate and degrees of freedom (DoF) performance is analyzed. Two distributed matching approaches and one centralized assignment approach are proposed to find a good IA-Cell assignment in three scenrios with different backhaul overhead. Thirdly, under limited feedback, the thesis derives an upper bound of the residual interference to noise ratio (RINR), formulates and solves a corresponding DBA problem. Finally, numerical results show that the proposed GIA with optimized IA-Cell assignment and the DBA greatly outperforms the traditional GIA algorithm

A Comprehensive Survey on Resource Allocation for CRAN in 5G and Beyond Networks

The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non- Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions

Resource allocation in networks via coalitional games

The main goal of this dissertation is to manage resource allocation in network engineering problems and to introduce efficient cooperative algorithms to obtain high performance, ensuring fairness and stability. Specifically, this dissertation introduces new approaches for resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) wireless networks and in smart power grids by casting the problems to the coalitional game framework and by providing a constructive iterative algorithm based on dynamic learning theory.  Software Engineering (Software)Algorithms and the Foundations of Software technolog

Allocation of Communication and Computation Resources in Mobile Networks

Konvergence komunikačních a výpočetních technologií vedlo k vzniku Multi-Access Edge Computing (MEC). MEC poskytuje výpočetní výkon na tzv. hraně mobilních sítí (základnové stanice, jádro mobilní sítě), který lze využít pro optimalizaci mobilních sítí v reálném čase. Optimalizacev reálném čase je umožněna díky nízkému komunikačnímu zpoždění například v porovnání s Mobile Cloud Computing (MCC). Optimalizace mobilních sítí vyžaduje informace o mobilní síti od uživatelských zařízeních, avšak sběr těchto informací využívá komunikační prostředky, které jsou využívány i pro přenos uživatelských dat. Zvyšující se počet uživatelských zařízení, senzorů a taktéž komunikace vozidel tvoří překážku pro sběr informací o mobilních sítích z důvodu omezeného množství komunikačních prostředků. Tudíž je nutné navrhnout řešení, která umožní sběr těchto informací pro potřeby optimalizace mobilních sítí. V této práci je navrženo řešení pro komunikaci vysokého počtu zařízeních, které je postaveno na využití přímé komunikace mezi zařízeními. Pro motivování uživatelů, pro využití přeposílání dat pomocí přímé komunikace mezi uživateli je navrženo přidělování komunikačních prostředků jenž vede na přirozenou spolupráci uživatelů. Dále je provedena analýza spotřeby energie při využití přeposílání dat pomocí přímé komunikace mezi uživateli pro ukázání jejích výhod z pohledu spotřeby energie. Pro další zvýšení počtu komunikujících zařízení je využito mobilních létajících základových stanic (FlyBS). Pro nasazení FlyBS je navržen algoritmus, který hledá pozici FlyBS a asociaci uživatel k FlyBS pro zvýšení spokojenosti uživatelů s poskytovanými datovými propustnostmi. MEC lze využít nejen pro optimalizaci mobilních sítí z pohledu mobilních operátorů, ale taktéž uživateli mobilních sítí. Tito uživatelé mohou využít MEC pro přenost výpočetně náročných úloh z jejich mobilních zařízeních do MEC. Z důvodu mobility uživatel je nutné nalézt vhodně přidělení komunikačních a výpočetních prostředků pro uspokojení uživatelských požadavků. Tudíž je navržen algorithmus pro výběr komunikační cesty mezi uživatelem a MEC, jenž je posléze rozšířen o přidělování výpočetných prostředků společně s komunikačními prostředky. Navržené řešení vede k snížení komunikačního zpoždění o desítky procent.The convergence of communication and computing in the mobile networks has led to an introduction of the Multi-Access Edge Computing (MEC). The MEC combines communication and computing resources at the edge of the mobile network and provides an option to optimize the mobile network in real-time. This is possible due to close proximity of the computation resources in terms of communication delay, in comparison to the Mobile Cloud Computing (MCC). The optimization of the mobile networks requires information about the mobile network and User Equipment (UE). Such information, however, consumes a significant amount of communication resources. The finite communication resources along with the ever increasing number of the UEs and other devices, such as sensors, vehicles pose an obstacle for collecting the required information. Therefore, it is necessary to provide solutions to enable the collection of the required mobile network information from the UEs for the purposes of the mobile network optimization. In this thesis, a solution to enable communication of a large number of devices, exploiting Device-to-Device (D2D) communication for data relaying, is proposed. To motivate the UEs to relay data of other UEs, we propose a resource allocation algorithm that leads to a natural cooperation of the UEs. To show, that the relaying is not only beneficial from the perspective of an increased number of UEs, we provide an analysis of the energy consumed by the D2D communication. To further increase the number of the UEs we exploit a recent concept of the flying base stations (FlyBSs), and we develop a joint algorithm for a positioning of the FlyBS and an association of the UEs to increase the UEs satisfaction with the provided data rates. The MEC can be exploited not only for processing of the collected data to optimize the mobile networks, but also by the mobile users. The mobile users can exploit the MEC for the computation offloading, i.e., transferring the computation from their UEs to the MEC. However, due to the inherent mobility of the UEs, it is necessary to determine communication and computation resource allocation in order to satisfy the UEs requirements. Therefore, we first propose a solution for a selection of the communication path between the UEs and the MEC (communication resource allocation). Then, we also design an algorithm for joint communication and computation resource allocation. The proposed solution then lead to a reduction in the computation offloading delay by tens of percent

Incentivizing Secondary Spectrum Trading: A Profit Perspective

With the ever-increasingly connected mobile devices, demand for mobile broadband service is likely to outstrip spectrum capacity in the near-term. Without action to address this spectrum crisis, service quality is likely to suffer and prices are likely to rise. Fortunately, recent studies show that a large part of licensed spectrum remains under-utilized, which should allow concepts such as dynamic spectrum access/sharing, open access, and secondary spectrum market to alleviate the crisis. From the inception of the open access paradigm, it was clear that for it to work two issues must be adequately addressed: sensing and pricing. The first refers to the ability of a device to accurately detect channel opportunity and more generally to acquire information on the spectrum environment. The second refers to mechanisms that provide license holders with the right incentives so that they will willingly allow access by unlicensed devices. For the pricing issue, we formulate a contract design problem where a primary license holder wishes to profit from its excess spectrum capacity by selling it to potential secondary users. It needs to determine how to optimally price the excess spectrum so as to maximize its profit, knowing that this excess capacity is stochastic in nature, does not come with exclusive access. We adopt as a reference a traditional spectrum market where the buyer can purchase exclusive access with fixed/deterministic guarantees. When multiple primary holders exist, we develop a price competition model for the license holders selling on a secondary spectrum market. We introduce a regulator which can also be thought of as the sellers forming a coalition, whose role is to enable money transfer based on partial observations of the sellers' actions. We show that by proper design of the transfer mechanism, efficient equilibrium (profit-maximizing) can be achieved. On the sensing front, we propose a spectrum utilization model which uses stochastic differential equations (SDE) to model dynamic scattering and multipath fading channels, in particular, Rayleigh-distributed stationary channels. The SDE model can generate spectrum dynamics as a temporal process, and is shown to provide very good fit for real spectrum measurement data.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/109017/1/shangpin_1.pd