Bandwidth (signal processing)

About: Bandwidth (signal processing) is a research topic. Over the lifetime, 48550 publications have been published within this topic receiving 600741 citations. The topic is also known as: Bandwidth (signal processing) & bandwidth.

Analysis of the effects of phase-noise in orthogonal frequency division multiplex (OFDM) systems

Abstract: In OFDM transmission schemes, phase-noise from oscillator instabilities in the receiver is a potentially serious problem, especially when bandwidth efficient, high order signal constellations are employed. The paper analyses the two effects of phase-noise: inter-carrier interference (ICI) and a phase error common to all OFDM sub-carriers. Through numerical integration, the ICI power can be evaluated and is shown as a function of the number of OFDM sub-carriers and various parameters of the phase-noise model. Increasing the number of sub-carriers causes an increase in the ICI power, which our analysis indeed shows to become a potential problem, since it can lead to a BER floor. The analysis allows the design of low-cost tuners through specifying the required phase-noise characteristics. A similar technique is applied to calculate the variance of the common phase error. After showing that the common phase error is essentially uncorrelated from symbol to symbol, we propose a simple feed-forward correction technique based on pilot cells, that dramatically reduces the degradation due to phase-noise. This is confirmed by BER simulations of a coded OFDM scheme (proposed for terrestrial transmission of digital television) with 64 QAM.

Theory and design of broadband sensor arrays with frequency invariant far‐field beam patterns

Abstract: The theory and design of a broadband array of sensors with a frequency invariant far‐field beam pattern over an arbitrarily wide design bandwidth is presented. The frequency invariant beam pattern property is defined in terms of a continuously distributed sensor, and the problem of designing a practical sensor array is then treated as an approximation to this continuous sensor using a discrete set of filtered broadband omnidirectional array elements. The design methodology is suitable for one‐, two‐, and three‐dimensional sensor arrays; it imposes no restrictions on the desired aperture distribution (beam shape), and can cope with arbitrarily wide bandwidths. An important consequence of the results is that the frequency response of the filter applied to the output of each sensor can be factored into two components: One component is related to a slice of the desired aperture distribution, and the other is sensor independent. The results also indicate that the locations of the sensors are not a crucial design consideration, although it is shown that nonuniform spacings simultaneously avoid spatial aliasing and minimize the number of sensors. An example design which covers a 10:1 frequency range (which is suitable for speech acquisition using a microphone array) illustrates the utility of the method. Finally, the theory is generalized to cover a parameterized class of arrays in which the frequency dependence of the beam pattern can be controlled in a continuous manner from a classical single‐frequency design to a frequency invariant design.

Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems

Abstract: When there are a large number of antennas in massive MIMO systems, the transmitted wideband signal will be sensitive to the physical propagation delay of electromagnetic waves across the large array aperture, which is called the spatial-wideband effect. In this scenario, the transceiver design is different from most of the existing works, which presume that the bandwidth of the transmitted signals is not that wide, ignore the spatial-wideband effect, and only address the frequency selectivity. In this paper, we investigate spatial- and frequency-wideband effects, called dual-wideband effects in massive MIMO systems from the array signal processing point of view. Taking millimeter-wave-band communications as an example, we describe the transmission process to address the dual-wideband effects. By exploiting the channel sparsity in the angle domain and the delay domain, we develop the efficient uplink and downlink channel estimation strategies that require much less amount of training overhead and cause no pilot contamination. Thanks to the array signal processing techniques, the proposed channel estimation is suitable for both TDD and FDD massive MIMO systems. Numerical examples demonstrate that the proposed transmission design for massive MIMO systems can effectively deal with the dual-wideband effects.

Optical frequency combs: Coherently uniting the electromagnetic spectrum.

Abstract: Optical frequency combs were introduced around 20 years ago as a laser technology that could synthesize and count the ultrafast rate of the oscillating cycles of light. Functioning in a manner analogous to a clockwork of gears, the frequency comb phase-coherently upconverts a radio frequency signal by a factor of [Formula: see text] to provide a vast array of evenly spaced optical frequencies, which is the comb for which the device is named. It also divides an optical frequency down to a radio frequency, or translates its phase to any other optical frequency across hundreds of terahertz of bandwidth. We review the historical backdrop against which this powerful tool for coherently uniting the electromagnetic spectrum developed. Advances in frequency comb functionality, physical implementation, and application are also described.

Cyclic Feature Detection With Sub-Nyquist Sampling for Wideband Spectrum Sensing

Abstract: For cognitive radio networks, efficient and robust spectrum sensing is a crucial enabling step for dynamic spectrum access. Cognitive radios need to not only rapidly identify spectrum opportunities over very wide bandwidth, but also make reliable decisions in noise-uncertain environments. Cyclic spectrum sensing techniques work well under noise uncertainty, but require high-rate sampling which is very costly in the wideband regime. This paper develops robust and compressive wideband spectrum sensing techniques by exploiting the unique sparsity property of the two-dimensional cyclic spectra of communications signals. To do so, a new compressed sensing framework is proposed for extracting useful second-order statistics of wideband random signals from digital samples taken at sub-Nyquist rates. The time-varying cross-correlation functions of these compressive samples are formulated to reveal the cyclic spectrum, which is then used to simultaneously detect multiple signal sources over the entire wide band. Because the proposed wideband cyclic spectrum estimator utilizes all the cross-correlation terms of compressive samples to extract second-order statistics, it is also able to recover the power spectra of stationary signals as a special case, permitting lossless rate compression even for non-sparse signals. Simulation results demonstrate the robustness of the proposed spectrum sensing algorithms against both sampling rate reduction and noise uncertainty in wireless networks.