CROSS-LAYER RESOURCE ALLOCATION SCHEME UNDER HETEROGENEOUS CONSTRAINTS FOR NEXT GENERATION HIGH RATE WPAN

International audienceIn the next generation wireless networks, the growing demand for new wireless applications is accompanied with high expectations for better quality of service (QoS) fulfillment especially for multimedia applications. Furthermore, the coexistence of future unlicensed users with existing licensed users is becoming a challenging task in next generation communication systems to overcome the underutilization of the spectrum. A QoS and interference aware resource allocation is thus of special interest in order to respond to the heterogeneous constraints of the next generation networks. In this work, we address the issue of resource allocation under heterogeneous constraints for unlicensed multi-band ultra-wideband (UWB) systems in the context of Future Home Networks, i.e. WPAN. The problem is first studied analytically using a heterogeneous constrained optimization problem formulation. After studying the characteristics of the optimal solution, we propose a low-complexity suboptimal algorithm based on a cross-layer approach that combines information provided by the PHY and MAC layers. While the PHY layer is responsible for providing the channel quality of the unlicensed UWB users as well as their interference power that they cause on licensed users, the MAC layer is responsible for classifying the unlicensed users using a two-class based approach that guarantees for multimedia services a high-priority level compared to other services. Combined in an efficient and simple way, the PHY and MAC information present the key elements of the aimed resource allocation. Simulation results demonstrate that the proposed scheme provides a good tradeoff between the QoS satisfaction of the unlicensed applications with hard QoS requirements and the limitation of the interference affecting the licensed users

CELLULAR-ENABLED MACHINE TYPE COMMUNICATIONS: RECENT TECHNOLOGIES AND COGNITIVE RADIO APPROACHES

The scarcity of bandwidth has always been the main obstacle for providing reliable high data-rate wireless links, which are in great demand to accommodate nowadays and immediate future wireless applications. In addition, recent reports have showed inefficient usage and under-utilization of the available bandwidth. Cognitive radio (CR) has recently emerged as a promising solution to enhance the spectrum utilization, where it offers the ability for unlicensed users to access the licensed spectrum opportunistically. By allowing opportunistic spectrum access which is the main concept for the interweave network model, the overall spectrum utilization can be improved. This requires cognitive radio networks (CRNs) to consider the spectrum sensing and monitoring as an essential enabling process for the interweave network model. Machine-to-machine (M2M) communication, which is the basic enabler for the Internet-of-Things (IoT), has emerged to be a key element in future networks. Machines are expected to communicate with each other exchanging information and data without human intervention. The ultimate objective of M2M communications is to construct comprehensive connections among all machines distributed over an extensive coverage area. Due to the radical change in the number of users, the network has to carefully utilize the available resources in order to maintain reasonable quality-of-service (QoS). Generally, one of the most important resources in wireless communications is the frequency spectrum. To utilize the frequency spectrum in IoT environment, it can be argued that cognitive radio concept is a possible solution from the cost and performance perspectives. Thus, supporting numerous number of machines is possible by employing dual-mode base stations which can apply cognitive radio concept in addition to the legacy licensed frequency assignment. In this thesis, a detailed review of the state of the art related to the application of spectrum sensing in CR communications is considered. We present the latest advances related to the implementation of the legacy spectrum sensing approaches. We also address the implementation challenges for cognitive radios in the direction of spectrum sensing and monitoring. We propose a novel algorithm to solve the reduced throughput issue due to the scheduled spectrum sensing and monitoring. Further, two new architectures are considered to significantly reduce the power consumption required by the CR to enable wideband sensing. Both systems rely on the 1-bit quantization at the receiver side. The system performance is analytically investigated and simulated. Also, complexity and power consumption are investigated and studied. Furthermore, we address the challenges that are expected from the next generation M2M network as an integral part of the future IoT. This mainly includes the design of low-power low-cost machine with reduced bandwidth. The trade-off between cost, feasibility, and performance are also discussed. Because of the relaxation of the frequency and spatial diversities, in addition, to enabling the extended coverage mode, initial synchronization and cell search have new challenges for cellular-enabled M2M systems. We study conventional solutions with their pros and cons including timing acquisition, cell detection, and frequency offset estimation algorithms. We provide a technique to enhance the performance in the presence of the harsh detection environment for LTE-based machines. Furthermore, we present a frequency tracking algorithm for cellular M2M systems that utilizes the new repetitive feature of the broadcast channel symbols in next generation Long Term Evolution (LTE) systems. In the direction of narrowband IoT support, we propose a cell search and initial synchronization algorithm that utilizes the new set of narrowband synchronization signals. The proposed algorithms have been simulated at very low signal to noise ratios and in different fading environments

Design, Modeling, and Analysis for MAC Protocols in Ultra-wideband Networks

Ultra-wideband (UWB) is an appealing transmission technology for short-range, bandwidth demanded wireless communications. With the data rate of several hundred megabits per second, UWB demonstrates great potential in supporting multimedia streams such as high-definition television (HDTV), voice over Internet Protocol (VoIP), and console gaming in office or home networks, known as the wireless personal area network (WPAN). While vast research effort has been made on the physical layer issues of UWB, the corresponding medium access control (MAC) protocols that exploit UWB technology have not been well developed. Given an extremely wide bandwidth of UWB, a fundamental problem on how to manage multiple users to efficiently utilize the bandwidth is a MAC design issue. Without explicitly considering the physical properties of UWB, existing MAC protocols are not optimized for UWB-based networks. In addition, the limited processing capability of UWB devices poses challenges to the design of low-complexity MAC protocols. In this thesis, we comprehensively investigate the MAC protocols for UWB networks. The objective is to link the physical characteristics of UWB with the MAC protocols to fully exploit its advantage. We consider two themes: centralized and distributed UWB networks. For centralized networks, the most critical issue surrounding the MAC protocol is the resource allocation with fairness and quality of service (QoS) provisioning. We address this issue by breaking down into two scenarios: homogeneous and heterogeneous network configurations. In the homogeneous case, users have the same bandwidth requirement, and the objective of resource allocation is to maximize the network throughput. In the heterogeneous case, users have different bandwidth requirements, and the objective of resource allocation is to provide differentiated services. For both design objectives, the optimal scheduling problem is NP-hard. Our contributions lie in the development of low-complexity scheduling algorithms that fully exploit the characteristics of UWB. For distributed networks, the MAC becomes node-based problems, rather than link-based problems as in centralized networks. Each node either contends for channel access or reserves transmission opportunity through negotiation. We investigate two representative protocols that have been adopted in the WiMedia specification for future UWB-based WPANs. One is a contention-based protocol called prioritized channel access (PCA), which employs the same mechanisms as the enhanced distributed channel access (EDCA) in IEEE 802.11e for providing differentiated services. The other is a reservation-based protocol called distributed reservation protocol (DRP), which allows time slots to be reserved in a distributed manner. Our goal is to identify the capabilities of these two protocols in supporting multimedia applications for UWB networks. To achieve this, we develop analytical models and conduct detailed analysis for respective protocols. The proposed analytical models have several merits. They are accurate and provide close-form expressions with low computational effort. Through a cross-layer approach, our analytical models can capture the near-realistic protocol behaviors, thus useful insights into the protocol can be obtained to improve or fine-tune the protocol operations. The proposed models can also be readily extended to incorporate more sophisticated considerations, which should benefit future UWB network design

Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose

Bit-Error-Rate-Minimizing Channel Shortening Using Post-FEQ Diversity Combining and a Genetic Algorithm

In advanced wireline or wireless communication systems, i.e., DSL, IEEE 802.11a/g, HIPERLAN/2, etc., a cyclic prefix which is proportional to the channel impulse response is needed to append a multicarrier modulation (MCM) frame for operating the MCM accurately. This prefix is used to combat inter symbol interference (ISI). In some cases, the channel impulse response can be longer than the cyclic prefix (CP). One of the most useful techniques to mitigate this problem is reuse of a Channel Shortening Equalizer (CSE) as a linear preprocessor before the MCM receiver in order to shorten the effective channel length. Channel shortening filter design is a widely examined topic in the literature. Most channel shortening equalizer proposals depend on perfect channel state information (CSI). However, this information may not be available in all situations. In cases where channel state information is not needed, blind adaptive equalization techniques are appropriate. In wireline communication systems (such as DMT), the CSE design is based on maximizing the bit rate, but in wireless systems (OFDM), there is a fixed bit loading algorithm, and the performance metric is Bit Error Rate (BER) minimization. In this work, a CSE is developed for multicarrier and single-carrier cyclic prefixed (SCCP) systems which attempts to minimize the BER. To minimize the BER, a Genetic Algorithm (GA), which is an optimization method based on the principles of natural selection and genetics, is used. If the CSI is shorter than the CP, the equalization can be done by a frequency domain equalizer (FEQ), which is a bank of complex scalars. However, in the literature the adaptive FEQ design has not been well examined. The second phase of this thesis focuses on different types of algorithms for adapting the FEQ and modifying the FEQ architecture to obtain a lower BER. Simulation results show that this modified architecture yields a 20 dB improvement in BER

Compressive Sensing of Multiband Spectrum towards Real-World Wideband Applications.

PhD Theses.Spectrum scarcity is a major challenge in wireless communication systems with their rapid evolutions towards more capacity and bandwidth. The fact that the real-world spectrum, as a nite resource, is sparsely utilized in certain bands spurs the proposal of spectrum sharing. In wideband scenarios, accurate real-time spectrum sensing, as an enabler of spectrum sharing, can become ine cient as it naturally requires the sampling rate of the analog-to-digital conversion to exceed the Nyquist rate, which is resourcecostly and energy-consuming. Compressive sensing techniques have been applied in wideband spectrum sensing to achieve sub-Nyquist-rate sampling of frequency sparse signals to alleviate such burdens. A major challenge of compressive spectrum sensing (CSS) is the complexity of the sparse recovery algorithm. Greedy algorithms achieve sparse recovery with low complexity but the required prior knowledge of the signal sparsity. A practical spectrum sparsity estimation scheme is proposed. Furthermore, the dimension of the sparse recovery problem is proposed to be reduced, which further reduces the complexity and achieves signal denoising that promotes recovery delity. The robust detection of incumbent radio is also a fundamental problem of CSS. To address the energy detection problem in CSS, the spectrum statistics of the recovered signals are investigated and a practical threshold adaption scheme for energy detection is proposed. Moreover, it is of particular interest to seek the challenges and opportunities to implement real-world CSS for systems with large bandwidth. Initial research on the practical issues towards the real-world realization of wideband CSS system based on the multicoset sampler architecture is presented. In all, this thesis provides insights into two critical challenges - low-complexity sparse recovery and robust energy detection - in the general CSS context, while also looks into some particular issues towards the real-world CSS implementation based on the i multicoset sampler