118,463 research outputs found
Space-Time Equalisation Assisted Minimum Bit-Error Ratio Multiuser Detection for SDMA Systems
This contribution investigates a space-time equalisation assisted multiuser detection scheme designed for multiple receiver antenna aided space division multiple access (SDMA) systems. A novel minimum bit error ratio (MBER) design is invoked for the multiuser detector (MUD), which is shown to be capable of improving the attainable performance and enhancing system capacity in comparison to that of the standard minimum mean square error (MMSE) design. The adaptive MUD coefficient adjustment procedure of the MBER space-time MUD is implemented using a stochastic gradient based least bit error rate (LBER) algorithm, which consistently outperforms the classic least mean square (LMS) algorithm, while maintaining a lower computational complexity than the latter
Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems
A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization of the channel and data estimation is decomposed into an iterative optimization loop. An efficient global optimization algorithm termed as the repeated weighted boosting aided search is employed first to identify the unknown SIMO channel model, and then the Viterbi algorithm is used for the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used for demonstrating the efficiency of this joint ML optimization scheme designed for blind adaptive SIMO systems
Random Matrix Models, Double-Time Painlev\'e Equations, and Wireless Relaying
This paper gives an in-depth study of a multiple-antenna wireless
communication scenario in which a weak signal received at an intermediate relay
station is amplified and then forwarded to the final destination. The key
quantity determining system performance is the statistical properties of the
signal-to-noise ratio (SNR) \gamma\ at the destination. Under certain
assumptions on the encoding structure, recent work has characterized the SNR
distribution through its moment generating function, in terms of a certain
Hankel determinant generated via a deformed Laguerre weight. Here, we employ
two different methods to describe the Hankel determinant. First, we make use of
ladder operators satisfied by orthogonal polynomials to give an exact
characterization in terms of a "double-time" Painlev\'e differential equation,
which reduces to Painlev\'e V under certain limits. Second, we employ Dyson's
Coulomb Fluid method to derive a closed form approximation for the Hankel
determinant. The two characterizations are used to derive closed-form
expressions for the cumulants of \gamma, and to compute performance quantities
of engineering interest.Comment: 72 pages, 6 figures; Minor typos corrected; Two additional lemmas
added in Appendix
A low-power opportunistic communication protocol for wearable applications
© 2015 IEEE.Recent trends in wearable applications demand flexible architectures being able to monitor people while they move in free-living environments. Current solutions use either store-download-offline processing or simple communication schemes with real-time streaming of sensor data. This limits the applicability of wearable applications to controlled environments (e.g, clinics, homes, or laboratories), because they need to maintain connectivity with the base station throughout the monitoring process. In this paper, we present the design and implementation of an opportunistic communication framework that simplifies the general use of wearable devices in free-living environments. It relies on a low-power data collection protocol that allows the end user to opportunistically, yet seamlessly manage the transmission of sensor data. We validate the feasibility of the framework by demonstrating its use for swimming, where the normal wireless communication is constantly interfered by the environment
Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing
Energy Harvesting Wireless Sensor Networks (EH- WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and net- working algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sens- ing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and eval- uate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs
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