Analog signal processing

Abstract: Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support occupies only a small portion of a wide spectrum. Our primary design goals are efficient hardware implementation and low computational load on the supporting digital processing. We propose a system, named the modulated wideband converter, which first multiplies the analog signal by a bank of periodic waveforms. The product is then low-pass filtered and sampled uniformly at a low rate, which is orders of magnitude smaller than Nyquist. Perfect recovery from the proposed samples is achieved under certain necessary and sufficient conditions. We also develop a digital architecture, which allows either reconstruction of the analog input, or processing of any band of interest at a low rate, that is, without interpolating to the high Nyquist rate. Numerical simulations demonstrate many engineering aspects: robustness to noise and mismodeling, potential hardware simplifications, real-time performance for signals with time-varying support and stability to quantization effects. We compare our system with two previous approaches: periodic nonuniform sampling, which is bandwidth limited by existing hardware devices, and the random demodulator, which is restricted to discrete multitone signals and has a high computational load. In the broader context of Nyquist sampling, our scheme has the potential to break through the bandwidth barrier of state-of-the-art analog conversion technologies such as interleaved converters.

Abstract: Hybrid analog/digital multiple-input multiple-output architectures were recently proposed as an alternative for fully digital-precoding in millimeter wave wireless communication systems. This is motivated by the possible reduction in the number of RF chains and analog-to-digital converters. In these architectures, the analog processing network is usually based on variable phase shifters. In this paper, we propose hybrid architectures based on switching networks to reduce the complexity and the power consumption of the structures based on phase shifters. We define a power consumption model and use it to evaluate the energy efficiency of both structures. To estimate the complete MIMO channel, we propose an open-loop compressive channel estimation technique that is independent of the hardware used in the analog processing stage. We analyze the performance of the new estimation algorithm for hybrid architectures based on phase shifters and switches. Using the estimate, we develop two algorithms for the design of the hybrid combiner based on switches and analyze the achieved spectral efficiency. Finally, we study the tradeoffs between power consumption, hardware complexity, and spectral efficiency for hybrid architectures based on phase shifting networks and switching networks. Numerical results show that architectures based on switches obtain equal or better channel estimation performance to that obtained using phase shifters, while reducing hardware complexity and power consumption. For equal power consumption, all the hybrid architectures provide similar spectral efficiencies.

Abstract: The Axial Double Probe (ADP) instrument measures the DC to ∼100 kHz electric field along the spin axis of the Magnetospheric Multiscale (MMS) spacecraft (Burch et al., Space Sci. Rev., 2014, this issue), completing the vector electric field when combined with the spin plane double probes (SDP) (Torbert et al., Space Sci. Rev., 2014, this issue, Lindqvist et al., Space Sci. Rev., 2014, this issue). Two cylindrical sensors are separated by over 30 m tip-to-tip, the longest baseline on an axial DC electric field ever attempted in space. The ADP on each of the spacecraft consists of two identical, 12.67 m graphite coilable booms with second, smaller 2.25 m booms mounted on their ends. A significant effort was carried out to assure that the potential field of the MMS spacecraft acts equally on the two sensors and that photo- and secondary electron currents do not vary over the spacecraft spin. The ADP on MMS is expected to measure DC electric field with a precision of ∼1 mV/m, a resolution of ∼25 μV/m, and a range of ∼±1 V/m in most of the plasma environments MMS will encounter. The Digital Signal Processing (DSP) units on the MMS spacecraft are designed to perform analog conditioning, analog-to-digital (A/D) conversion, and digital processing on the ADP, SDP, and search coil magnetometer (SCM) (Le Contel et al., Space Sci. Rev., 2014, this issue) signals. The DSP units include digital filters, spectral processing, a high-speed burst memory, a solitary structure detector, and data compression. The DSP uses precision analog processing with, in most cases, >100 dB in dynamic range, better that −80 dB common mode rejection in electric field (E) signal processing, and better that −80 dB cross talk between the E and SCM (B) signals. The A/D conversion is at 16 bits with ∼1/4 LSB accuracy and ∼1 LSB noise. The digital signal processing is powerful and highly flexible allowing for maximum scientific return under a limited telemetry volume. The ADP and DSP are described in this article.