8,181 research outputs found
Energy-Efficient Distributed Estimation by Utilizing a Nonlinear Amplifier
abstract: Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.Dissertation/ThesisPh.D. Electrical Engineering 201
Coherent terabit communications with microresonator Kerr frequency combs
Optical frequency combs enable coherent data transmission on hundreds of
wavelength channels and have the potential to revolutionize terabit
communications. Generation of Kerr combs in nonlinear integrated microcavities
represents a particularly promising option enabling line spacings of tens of
GHz, compliant with wavelength-division multiplexing (WDM) grids. However, Kerr
combs may exhibit strong phase noise and multiplet spectral lines, and this has
made high-speed data transmission impossible up to now. Recent work has shown
that systematic adjustment of pump conditions enables low phase-noise Kerr
combs with singlet spectral lines. Here we demonstrate that Kerr combs are
suited for coherent data transmission with advanced modulation formats that
pose stringent requirements on the spectral purity of the optical source. In a
first experiment, we encode a data stream of 392 Gbit/s on subsequent lines of
a Kerr comb using quadrature phase shift keying (QPSK) and 16-state quadrature
amplitude modulation (16QAM). A second experiment shows feedback-stabilization
of a Kerr comb and transmission of a 1.44 Tbit/s data stream over a distance of
up to 300 km. The results demonstrate that Kerr combs can meet the highly
demanding requirements of multi-terabit/s coherent communications and thus
offer a solution towards chip-scale terabit/s transceivers
Massive MIMO with Non-Ideal Arbitrary Arrays: Hardware Scaling Laws and Circuit-Aware Design
Massive multiple-input multiple-output (MIMO) systems are cellular networks
where the base stations (BSs) are equipped with unconventionally many antennas,
deployed on co-located or distributed arrays. Huge spatial degrees-of-freedom
are achieved by coherent processing over these massive arrays, which provide
strong signal gains, resilience to imperfect channel knowledge, and low
interference. This comes at the price of more infrastructure; the hardware cost
and circuit power consumption scale linearly/affinely with the number of BS
antennas . Hence, the key to cost-efficient deployment of large arrays is
low-cost antenna branches with low circuit power, in contrast to today's
conventional expensive and power-hungry BS antenna branches. Such low-cost
transceivers are prone to hardware imperfections, but it has been conjectured
that the huge degrees-of-freedom would bring robustness to such imperfections.
We prove this claim for a generalized uplink system with multiplicative
phase-drifts, additive distortion noise, and noise amplification. Specifically,
we derive closed-form expressions for the user rates and a scaling law that
shows how fast the hardware imperfections can increase with while
maintaining high rates. The connection between this scaling law and the power
consumption of different transceiver circuits is rigorously exemplified. This
reveals that one can make the circuit power increase as , instead of
linearly, by careful circuit-aware system design.Comment: Accepted for publication in IEEE Transactions on Wireless
Communications, 16 pages, 8 figures. The results can be reproduced using the
following Matlab code: https://github.com/emilbjornson/hardware-scaling-law
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