239 research outputs found

    Characterization of Coded Random Access with Compressive Sensing based Multi-User Detection

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    The emergence of Machine-to-Machine (M2M) communication requires new Medium Access Control (MAC) schemes and physical (PHY) layer concepts to support a massive number of access requests. The concept of coded random access, introduced recently, greatly outperforms other random access methods and is inherently capable to take advantage of the capture effect from the PHY layer. Furthermore, at the PHY layer, compressive sensing based multi-user detection (CS-MUD) is a novel technique that exploits sparsity in multi-user detection to achieve a joint activity and data detection. In this paper, we combine coded random access with CS-MUD on the PHY layer and show very promising results for the resulting protocol.Comment: Submitted to Globecom 201

    Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks

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    The fifth generation of cellular communication systems is foreseen to enable a multitude of new applications and use cases with very different requirements. A new 5G multiservice air interface needs to enhance broadband performance as well as provide new levels of reliability, latency and supported number of users. In this paper we focus on the massive Machine Type Communications (mMTC) service within a multi-service air interface. Specifically, we present an overview of different physical and medium access techniques to address the problem of a massive number of access attempts in mMTC and discuss the protocol performance of these solutions in a common evaluation framework

    Rapid Digital Architecture Design of Computationally Complex Algorithms

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    Traditional digital design techniques hardly keep up with the rising abundance of programmable circuitry found on recent Field-Programmable Gate Arrays. Therefore, the novel Rapid Data Type-Agnostic Digital Design Methodology (RDAM) elevates the design perspective of digital design engineers away from the register-transfer level to the algorithmic level. It is founded on the capabilities of High-Level Synthesis tools. By consequently working with data type-agnostic source codes, the RDAM brings significant simplifications to the fixed-point conversion of algorithms and the design of complex-valued architectures. Signal processing applications from the field of Compressed Sensing illustrate the efficacy of the RDAM in the context of multi-user wireless communications. For instance, a complex-valued digital architecture of Orthogonal Matching Pursuit with rank-1 updating has successfully been implemented and tested

    Design of large polyphase filters in the Quadratic Residue Number System

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    Compression and Recovery in Cell-free Cloud Radio Access Network

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    Cloud radio access network (C-RAN) is an evolved network architecture for future mobile communication systems. It aims to provide higher spectral efficiency, lower energy consumption and reduced cost of operations and maintenance for the network, which will enable the operators to not only satisfy growing user demands, but provide new services and applications. However, the huge load on the fronthaul network which connects the baseband unit (BBU) and a large number of remote radio heads (RRHs) is a significant challenge. To improve the fronthaul performance, a data compression and recovery scheme based on compressive sensing is proposed in this thesis. First, the theory of compressive sensing is studied, including the essential principles, standard compressive sensing model, potential measurement matrices, etc. Several popular recovery algorithms in compressive sensing are demonstrated in detail. Secondly, a compression and recovery scheme is proposed for the uplink of a cell-free C-RAN system. In the proposed scheme, compressive sensing is applied by exploiting the sparsity of user data. In particular, the multi-access fading in this system is incorporated into the formulation of the compressive sensing model. The aggregated measurement matrix which contains both the channel matrix and the fronthaul compression matrix is shown to satisfy the restricted isometry property (RIP) condition. Furthermore, two different recovery algorithms, basis pursuit denoising (BPDN) and sparsity adaptive matching pursuit (SAMP), are used respectively for estimating the sparse signals. The major advantage is that they do not require the sparsity of user data as a prior information during the process of signal recovery. It allows easy applications in many practical scenarios where the number of non-zero elements of the signals is not available. The simulation results show that the proposed scheme can efficiently alleviate the heavy burden on the fronthaul network, and meanwhile provide stable signal recovery for this system

    Spectrum Adaptation in Cognitive Radio Systems with Operating Constraints

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    The explosion of high-data-rate-demanding wireless applications such as smart-phones and wireless Internet access devices, together with growth of existing wireless services, are creating a shortage of the scarce Radio Frequency (RF) spectrum. However, several spectrum measurement campaigns revealed that current spectrum usage across time and frequency is inefficient, creating the artificial shortage of the spectrum because of the traditional exclusive command-and-control model of using the spectrum. Therefore, a new concept of Cognitive Radio (CR) has been emerging recently in which unlicensed users temporarily borrow spectrum from the licensed Primary Users (PU) based on the Dynamic Spectrum Access (DSA) technique that is also known as the spectrum sharing concept. A CR is an intelligent radio system based on the Software Defined Radio platform with artificial intelligence capability which can learn, adapt, and reconfigure through interaction with the operating environment. A CR system will revolutionize the way people share the RF spectrum, lowering harmful interference to the licensed PU of the spectrum, fostering innovative DSA technology and giving people more choices when it comes to using the wireless-communication-dependent applications without having any spectrum congestion problems. A key technical challenge for enabling secondary access to the licensed spectrum adaptation is to ensure that the CR does not interfere with the licensed incumbent users. However, incumbent user behavior is dynamic and requires CR systems to adapt this behavior in order to maintain smooth information transmission. In this context, the objective of this dissertation is to explore design issues for CR systems focusing on adaptation of physical layer parameters related to spectrum sensing, spectrum shaping, and rate/power control. Specifically, this dissertation discusses dynamic threshold adaptation for energy detector spectrum sensing, spectrum allocation and power control in Orthogonal Frequency Division Multiplexing-(OFDM-)based CR with operating constraints, and adjacent band interference suppression techniques in turbo-coded OFDM-based CR systems

    A Unified Multi-Functional Dynamic Spectrum Access Framework: Tutorial, Theory and Multi-GHz Wideband Testbed

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    Dynamic spectrum access is a must-have ingredient for future sensors that are ideally cognitive. The goal of this paper is a tutorial treatment of wideband cognitive radio and radar—a convergence of (1) algorithms survey, (2) hardware platforms survey, (3) challenges for multi-function (radar/communications) multi-GHz front end, (4) compressed sensing for multi-GHz waveforms—revolutionary A/D, (5) machine learning for cognitive radio/radar, (6) quickest detection, and (7) overlay/underlay cognitive radio waveforms. One focus of this paper is to address the multi-GHz front end, which is the challenge for the next-generation cognitive sensors. The unifying theme of this paper is to spell out the convergence for cognitive radio, radar, and anti-jamming. Moore’s law drives the system functions into digital parts. From a system viewpoint, this paper gives the first comprehensive treatment for the functions and the challenges of this multi-function (wideband) system. This paper brings together the inter-disciplinary knowledge

    Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

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    © 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements
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