Communications protocols for wireless sensor networks in perturbed environment

This thesis is mainly in the Smart Grid (SG) domain. SGs improve the safety of electrical networks and allow a more adapted use of electricity storage, available in a limited way. SGs also increase overall energy efficiency by reducing peak consumption. The use of this technology is the most appropriate solution because it allows more efficient energy management. In this context, manufacturers such as Hydro-Quebec deploy sensor networks in the nerve centers to control major equipment. To reduce deployment costs and cabling complexity, the option of a wireless sensor network seems the most obvious solution. However, deploying a sensor network requires in-depth knowledge of the environment. High voltages substations are strategic points in the power grid and generate impulse noise that can degrade the performance of wireless communications. The works in this thesis are focused on the development of high performance communication protocols for the profoundly disturbed environments. For this purpose, we have proposed an approach based on the concatenation of rank metric and convolutional coding with orthogonal frequency division multiplexing. This technique is very efficient in reducing the bursty nature of impulsive noise while having a quite low level of complexity. Another solution based on a multi-antenna system is also designed. We have proposed a cooperative closed-loop coded MIMO system based on rank metric code and max−dmin precoder. The second technique is also an optimal solution for both improving the reliability of the system and energy saving in wireless sensor networks

EXTRINSIC CHANNEL-LIKE FINGERPRINT EMBEDDING FOR TRANSMITTER AUTHENTICATION IN WIRELESS SYSTEMS

We present a physical-layer fingerprint-embedding scheme for wireless signals, focusing on multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) transmissions, where the fingerprint signal conveys a low capacity communication suitable for authenticating the transmission and further facilitating secure communications. Our system strives to embed the fingerprint message into the noise subspace of the channel estimates obtained by the receiver, using a number of signal spreading techniques. When side information of channel state is known and leveraged by the transmitter, the performance of the fingerprint embedding can be improved. When channel state information is not known, blind spreading techniques are applied. The fingerprint message is only visible to aware receivers who explicitly preform detection of the signal, but is invisible to receivers employing typical channel equalization. A taxonomy of overlay designs is discussed and these designs are explored through experiment using time-varying channel-state information (CSI) recorded from IEEE802.16e Mobile WiMax base stations. The performance of the fingerprint signal as received by a WiMax subscriber is demonstrated using CSI measurements derived from the downlink signal. Detection performance for the digital fingerprint message in time-varying channel conditions is also presented via simulation

Base station cooperation in multiple input multiple output orthogonal frequency division multiple access systems

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 60-62.Newly emerging advancements such as multiple input multiple output (MIMO) and orthogonal frequency division multiple access (OFDMA) techniques become indispensable parts of today’s wireless systems such as WiMAX (IEEE 802.16 standard) since they can increase the supportable data rates significantly. However, achieving the maximum spectral efficiency in a MIMO system requires perfect channel state information (CSI) at the transmitter side and multicarrier nature of OFDMA systems increase the necessary CSI feedback from users to base stations remarkably. To further increase the supportable data rates, using frequency reuse factor of 1 in the system is also mandatory. Unfortunately, this results in significant cochannel interference (CCI) observed especially by the users near cell edges, which can severely degrade the system spectral efficiency. To cope with this problem, base station cooperation may play an important role. In this thesis, the problem of cooperative data transmission from base stations to users in multicellular MIMO-OFDMA systems is considered. An efficient cooperative scheduling and data transmission scheme, requiring limited CSI feedback from users to base stations and also limited information exchange between the base stations, is proposed. The numerical results demonstrate that, the proposed algorithm offers considerable spectral efficiency gains compared to conventional frequency reuse and noncooperative schemes, under severe CCI conditionsTokel, Turgut BarışM.S

The optimization of multiple antenna broadband wireless communications. A study of propagation, space-time coding and spatial envelope correlation in Multiple Input, Multiple Output radio systems

This work concentrates on the application of diversity techniques and space time block coding for future mobile wireless communications. The initial system analysis employs a space-time coded OFDM transmitter over a multipath Rayleigh channel, and a receiver which uses a selection combining diversity technique. The performance of this combined scenario is characterised in terms of the bit error rate and throughput. A novel four element QOSTBC scheme is introduced, it is created by reforming the detection matrix of the original QOSTBC scheme, for which an orthogonal channel matrix is derived. This results in a computationally less complex linear decoding scheme as compared with the original QOSTBC. Space time coding schemes for three, four and eight transmitters were also derived using a Hadamard matrix. The practical optimization of multi-antenna networks is studied for realistic indoor and mixed propagation scenarios. The starting point is a detailed analysis of the throughput and field strength distributions for a commercial dual band 802.11n MIMO radio operating indoors in a variety of line of sight and non-line of sight scenarios. The physical model of the space is based on architectural schematics, and realistic propagation data for the construction materials. The modelling is then extended and generalized to a multi-storey indoor environment, and a large mixed site for indoor and outdoor channels based on the Bradford University campus. The implications for the physical layer are also explored through the specification of antenna envelope correlation coefficients. Initially this is for an antenna module configuration with two independent antennas in close proximity. An operational method is proposed using the scattering parameters of the system and which incorporates the intrinsic power losses of the radiating elements. The method is extended to estimate the envelope correlation coefficient for any two elements in a general (N,N) MIMO antenna array. Three examples are presented to validate this technique, and very close agreement is shown to exist between this method and the full electromagnetic analysis using the far field antenna radiation patterns

Transmitter Optimization Techniques for Physical Layer Security

Information security is one of the most critical issues in wireless networks as the signals transmitted through wireless medium are more vulnerable for interception. Although the existing conventional security techniques are proven to be safe, the broadcast nature of wireless communications introduces different challenges in terms of key exchange and distributions. As a result, information theoretic physical layer security has been proposed to complement the conventional security techniques for enhancing security in wireless transmissions. On the other hand, the rapid growth of data rates introduces different challenges on power limited mobile devices in terms of energy requirements. Recently, research work on wireless power transfer claimed that it has been considered as a potential technique to extend the battery lifetime of wireless networks. However, the algorithms developed based on the conventional optimization approaches often require iterative techniques, which poses challenges for real-time processing. To meet the demanding requirements of future ultra-low latency and reliable networks, neural network (NN) based approach can be employed to determine the resource allocations in wireless communications. This thesis developed different transmission strategies for secure transmission in wireless communications. Firstly, transmitter designs are focused in a multiple-input single-output simultaneous wireless information and power transfer system with unknown eavesdroppers. To improve the performance of physical layer security and the harvested energy, artificial noise is incorporated into the network to mask the secret information between the legitimate terminals. Then, different secrecy energy efficiency designs are considered for a MISO underlay cognitive radio network, in the presence of an energy harvesting receiver. In particular, these designs are developed with different channel state information assumptions at the transmitter. Finally, two different power allocation designs are investigated for a cognitive radio network to maximize the secrecy rate of the secondary receiver: conventional convex optimization framework and NN based algorithm

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

Advanced receivers for distributed cooperation in mobile ad hoc networks

Mobile ad hoc networks (MANETs) are rapidly deployable wireless communications systems, operating with minimal coordination in order to avoid spectral efficiency losses caused by overhead. Cooperative transmission schemes are attractive for MANETs, but the distributed nature of such protocols comes with an increased level of interference, whose impact is further amplified by the need to push the limits of energy and spectral efficiency. Hence, the impact of interference has to be mitigated through with the use PHY layer signal processing algorithms with reasonable computational complexity. Recent advances in iterative digital receiver design techniques exploit approximate Bayesian inference and derivative message passing techniques to improve the capabilities of well-established turbo detectors. In particular, expectation propagation (EP) is a flexible technique which offers attractive complexity-performance trade-offs in situations where conventional belief propagation is limited by computational complexity. Moreover, thanks to emerging techniques in deep learning, such iterative structures are cast into deep detection networks, where learning the algorithmic hyper-parameters further improves receiver performance. In this thesis, EP-based finite-impulse response decision feedback equalizers are designed, and they achieve significant improvements, especially in high spectral efficiency applications, over more conventional turbo-equalization techniques, while having the advantage of being asymptotically predictable. A framework for designing frequency-domain EP-based receivers is proposed, in order to obtain detection architectures with low computational complexity. This framework is theoretically and numerically analysed with a focus on channel equalization, and then it is also extended to handle detection for time-varying channels and multiple-antenna systems. The design of multiple-user detectors and the impact of channel estimation are also explored to understand the capabilities and limits of this framework. Finally, a finite-length performance prediction method is presented for carrying out link abstraction for the EP-based frequency domain equalizer. The impact of accurate physical layer modelling is evaluated in the context of cooperative broadcasting in tactical MANETs, thanks to a flexible MAC-level simulato