Interference mitigation in cognitive femtocell networks

“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Femtocells have been introduced as a solution to poor indoor coverage in cellular communication which has hugely attracted network operators and stakeholders. However, femtocells are designed to co-exist alongside macrocells providing improved spatial frequency reuse and higher spectrum efficiency to name a few. Therefore, when deployed in the two-tier architecture with macrocells, it is necessary to mitigate the inherent co-tier and cross-tier interference. The integration of cognitive radio (CR) in femtocells introduces the ability of femtocells to dynamically adapt to varying network conditions through learning and reasoning. This research work focuses on the exploitation of cognitive radio in femtocells to mitigate the mutual interference caused in the two-tier architecture. The research work presents original contributions in mitigating interference in femtocells by introducing practical approaches which comprises a power control scheme where femtocells adaptively controls its transmit power levels to reduce the interference it causes in a network. This is especially useful since femtocells are user deployed as this seeks to mitigate interference based on their blind placement in an indoor environment. Hybrid interference mitigation schemes which combine power control and resource/scheduling are also implemented. In a joint threshold power based admittance and contention free resource allocation scheme, the mutual interference between a Femtocell Access Point (FAP) and close-by User Equipments (UE) is mitigated based on admittance. Also, a hybrid scheme where FAPs opportunistically use Resource Blocks (RB) of Macrocell User Equipments (MUE) based on its traffic load use is also employed. Simulation analysis present improvements when these schemes are applied with emphasis in Long Term Evolution (LTE) networks especially in terms of Signal to Interference plus Noise Ratio (SINR)

Cognitive-Empowered Femtocells: An Intelligent Paradigm of a Robust and Efficient Media Access

Driven by both the need for ubiquitous wireless services and the stringent strain on radio spectrum faced in today's wireless communications, cognitive radio (CR) have been investigated as a promising solution to deploy Wireless Regional Area Networks (WRANs) for an efficient spectrum utilization. Communication devices with CR capabilities are able to access spectrum bands licensed for other wireless services in an opportunistic and secondary fashion, while preventing harmful interference to incumbent licensed services. However, a lesson learned from early experiences in developing such macro-cellular networks is that it becomes increasingly less economically viable to develop CR macrocellular infrastructures for increasing data rates in both line-of-sight as well as non-line-of-sight situation of WRAN, and the corresponding quality of service (QoS) in macrocellular networks is also noticeably degraded due to path loss, shadowing, and multipath fading due to wall penetration. Moreover, there are several challenges to make the real-world CR enabling dynamic spectrum access a difficult problem to implement without harmful interference. First, the hardware design of cognitive radio on the physical layer involves the tuning over a broad range of spectrum to detect a weak signal in a dynamic environment of fading channels, which in turn makes identification of the spectrum opportunities hard to achieve in an efficient and accurate manner. Second, opportunistic media access based on imperfect spectrum usage information obtain from physical layer brings up undesirable interference issue, as well as reliability issues introduced by mutual interference. Third, the curial issue is to determine which channels to use for data transmissions in presence of the dynamic and opportunistic nature of wireless environments, in the case where pre-defined dedicated control channel is not available in the complex and heterogenous networks. In this dissertation, a novel framework called Cognitive-Empowered Femtocell (CEF), which combines CR techniques with femtocell networking, is introduced to tackle these challenges and achieve better spectrum reuse, lower interference, easy integration, wider network coverage, as well as fast and cost effective early stage WRAN. In this framework, a sensing coordination scheme is proposed to gracefully unshackles the master/slave relationship between central controllers and end users, while maintaining order and coordination such that better sensing precision and efficiency can be achieved. As such, the network intelligence can be expanded from controlling the intelligence paradigm to better understand the satisfy wireless user needs. We also discuss design and deployment aspects such as sensing with reasoning approach, gossip-enabled stochastic media access without a dedicated control channel, all of which are important to the success of the CEF framework. We illustrate that such a framework allows wireless users to intelligently capture spectrum opportunities while mitigating interference to other users, as well as improving the network capacity. Performance analysis and simulations were conducted based on these techniques to provide insight on the future direction of interference suppression for dynamic spectrum access

Towards 5G wireless systems: A modified Rake receiver for UWB indoor multipath channels

This paper presents a modified receiver based on the conventional Rake receiver for Ultra-Wide Band (UWB) indoor channels of femtocell systems and aims to propose a new solution to mitigate the multipath phenomenon. Furthermore, this work proposes an upgrade for the conventional Rake receiver to fulfill the needs of 5G wireless systems through a new concept named “hybrid femtocell” that joins UWB with millimeter wave (mmWave) signals. The modified receiver is considered to be a part of the UWB/mmWave hybrid femtocell system, where it is developed for confronting the indoor multipath channels and to ensure a flexible transmission based on an Intelligent Controlling System (ICS). Hence, we seek to exploit the circumstances when the channel is less complex to switch the transmission to a higher data rate through higher M-ary Pulse Position Modulation (PPM). Furthermore, an ICS algorithm is proposed and an analytical model is developed followed by performance studies through simulation results. The results show that using the UWB technology through the modified receiver in femtocells could aid in mitigating the multipath effects and ensuring high throughputs. Thus, the UWB based system promotes Internet of Things (IoT) devices in indoor multipath channels of future 5G

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe

Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments

Performance assessment & synergic operation of algorithmic solutions enabling opportunistic networks– D4.2

Deliverable D4.2 del projecte europeu OneFITPeer ReviewedPostprint (updated version

The electronically steerable parasitic array radiator antenna for wireless communications : signal processing and emerging techniques

Smart antenna technology is expected to play an important role in future wireless communication networks in order to use the spectrum efficiently, improve the quality of service, reduce the costs of establishing new wireless paradigms and reduce the energy consumption in wireless networks. Generally, smart antennas exploit multiple widely spaced active elements, which are connected to separate radio frequency (RF) chains. Therefore, they are only applicable to base stations (BSs) and access points, by contrast with modern compact wireless terminals with constraints on size, power and complexity. This dissertation considers an alternative smart antenna system the electronically steerable parasitic array radiator (ESPAR) which uses only a single RF chain, coupled with multiple parasitic elements. The ESPAR antenna is of significant interest because of its flexibility in beamforming by tuning a number of easy-to-implement reactance loads connected to parasitic elements; however, parasitic elements require no expensive RF circuits. This work concentrates on the study of the ESPAR antenna for compact transceivers in order to achieve some emerging techniques in wireless communications. The work begins by presenting the work principle and modeling of the ESPAR antenna and describes the reactance-domain signal processing that is suited to the single active antenna array, which are fundamental factors throughout this thesis. The major contribution in this chapter is the adaptive beamforming method based on the ESPAR antenna. In order to achieve fast convergent beamforming for the ESPAR antenna, a modified minimum variance distortionless response (MVDR) beamfomer is proposed. With reactance-domain signal processing, the ESPAR array obtains a correlation matrix of receive signals as the input to the MVDR optimization problem. To design a set of feasible reactance loads for a desired beampattern, the MVDR optimization problem is reformulated as a convex optimization problem constraining an optimized weight vector close to a feasible solution. Finally, the necessary reactance loads are optimized by iterating the convex problem and a simple projector. In addition, the generic algorithm-based beamforming method has also studied for the ESPAR antenna. Blind interference alignment (BIA) is a promising technique for providing an optimal degree of freedom in a multi-user, multiple-inputsingle-output broadcast channel, without the requirements of channel state information at the transmitters. Its key is antenna mode switching at the receive antenna. The ESPAR antenna is able to provide a practical solution to beampattern switching (one kind of antenna mode switching) for the implementation of BIA. In this chapter, three beamforming methods are proposed for providing the required number of beampatterns that are exploited across one super symbol for creating the channel fluctuation patterns seen by receivers. These manually created channel fluctuation patterns are jointly combined with the designed spacetime precoding in order to align the inter-user interference. Furthermore, the directional beampatterns designed in the ESPAR antenna are demonstrated to improve the performance of BIA by alleviating the noise amplification. The ESPAR antenna is studied as the solution to interference mitigation in small cell networks. Specifically, ESPARs analog beamforming presented in the previous chapter is exploited to suppress inter-cell interference for the system scenario, scheduling only one user to be served by each small BS at a single time. In addition, the ESPAR-based BIA is employed to mitigate both inter-cell and intracell interference for the system scenario, scheduling a small number of users to be simultaneously served by each small BS for a single time. In the cognitive radio (CR) paradigm, the ESPAR antenna is employed for spatial spectrum sensing in order to utilize the new angle dimension in the spectrum space besides the conventional frequency, time and space dimensions. The twostage spatial spectrum sensing method is proposed based on the ESPAR antenna being targeted at identifying white spectrum space, including the new angle dimension. At the first stage, the occupancy of a specific frequency band is detected by conventional spectrum-sensing methods, including energy detector and eigenvalue-based methods implemented with the switched-beam ESPAR antenna. With the presence of primary users, their directions are estimated at the second stage, by high-resolution angle-of-arrival (AoA) estimation algorithms. Specifically, the compressive sensing technology has been studied for AoA detection with the ESPAR antenna, which is demonstrated to provide high-resolution estimation results and even to outperform the reactance-domain multiple signal classification

Modeling and Performance of Uplink Cache-Enabled Massive MIMO Heterogeneous Networks

A significant burden on wireless networks is brought by the uploading of user-generated contents to the Internet by means of applications such as social media. To cope with this mobile data tsunami, we develop a novel multiple-input multiple-output (MIMO) network architecture with randomly located base stations (BSs) a large number of antennas employing cache-enabled uplink transmission. In particular, we formulate a scenario, where the users upload their content to their strongest BSs, which are Poisson point process distributed. In addition, the BSs, exploiting the benefits of massive MIMO, upload their contents to the core network by means of a finite-rate backhaul. After proposing the caching policies, where we propose the modified von Mises distribution as the popularity distribution function, we derive the outage probability and the average delivery rate by taking advantage of tools from the deterministic equivalent and stochastic geometry analyses. Numerical results investigate the realistic performance gains of the proposed heterogeneous cache-enabled uplink on the network in terms of cardinal operating parameters. For example, insights regarding the BSs storage size are exposed. Moreover, the impacts of the key parameters such as the file popularity distribution and the target bitrate are investigated. Specifically, the outage probability decreases if the storage size is increased, while the average delivery rate increases. In addition, the concentration parameter, defining the number of files stored at the intermediate nodes (popularity), affects the proposed metrics directly. Furthermore, a higher target rate results in higher outage because fewer users obey this constraint. Also, we demonstrate that a denser network decreases the outage and increases the delivery rate. Hence, the introduction of caching at the uplink of the system design ameliorates the network performance.Peer reviewe

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Design, analysis and optimization of visible light communications based indoor access systems for mobile and internet of things applications

Demands for indoor broadband wireless access services are expected to outstrip the spectrum capacity in the near-term spectrum crunch . Deploying additional femtocells to address spectrum crunch is cost-inefficient due to the backhaul challenge and the exorbitant system maintenance. According to an Alcatel-Lucent report, most mobile Internet access traffic happens indoors. To alleviate the spectrum crunch and the backhaul challenge problems, visible light communication (VLC) emerges as an attractive candidate for indoor wireless access in the 5G architecture. In particular, VLC utilizes LED or fluorescent lamps to send out imperceptible flickering light that can be captured by a smart phone camera or photodetector. Leveraging power line communication and the available indoor infrastructure, VLC can be utilized with a small one-time cost. VLC also facilitates the great advantage of being able to jointly perform illumination and communications. Integration of VLC into the existing indoor wireless access networks embraces many challenges, such as lack of uplink infrastructure, excessive delay caused by blockage in heterogeneous networks, and overhead of power consumption. In addition, applying VLC to Internet-of-Things (IoT) applications, such as communication and localization, faces the challenges including ultra-low power requirement, limited modulation bandwidth, and heavy computation and sensing at the device end. In this dissertation, to overcome the challenges of VLC, a VLC enhanced WiFi system is designed by incorporating VLC downlink and WiFi uplink to connect mobile devices to the Internet. To further enhance robustness and throughput, WiFi and VLC are aggregated in parallel by leveraging the bonding technique in Linux operating system. Based on dynamic resource allocation, the delay performance of heterogeneous RF-VLC network is analyzed and evaluated for two different configurations - aggregation and non-aggregation. To mitigate the power consumption overhead of VLC, a problem of minimizing the total power consumption of a general multi-user VLC indoor network while satisfying users traffic demands and maintaining an acceptable level of illumination is formulated. The optimization problem is solved by the efficient column generation algorithm. With ultra-low power consumption, VLC backscatter harvests energy from indoor light sources and transmits optical signals by modulating the reflected light from a reflector. A novel pixelated VLC backscatter is proposed and prototyped to address the limited modulation bandwidth by enabling more advanced modulation scheme than the state-of-the-art on-off keying (OOK) scheme and allowing for the first time orthogonal multiple access. VLC-based indoor access system is also suitable for indoor localization due to its unique properties, such as utilization of existing ubiquitous lighting infrastructure, high location and orientation accuracy, and no interruption to RF-based devices. A novel retroreflector-based visible light localization system is proposed and prototyped to establish an almost zero-delay backward channel using a retroreflector to reflect light back to its source. This system can localize passive IoT devices without requiring computation and heavy sensing (e.g., camera) at the device end