2025 Theses Doctoral

LO-Path Modulated Receivers for Cognitive Radio and Phased Array Systems

Barac, Petar

Increasing demand for wireless connectivity at faster speeds and more devices is stressing thecurrent infrastructure as technologies such as autonomous driving, unmanned aerial vehicles, and satellite communications become more commercially available. There is a massive need for efficient use of already existing spectrum. Two methods to improve efficiency are implementing cognitive radio (CR) networks that autonomously take advantage of all available bands and resources, and phased array systems that allow for spatial diversity and the reuse of spectrum.

One design strategy in both methods is to use modulated local oscillator (LO) waveforms to drive the mixers in the receiver to provide either sensing capabilities, or beamforming of the phased array. Cognitive radio networks are an agile and robust solution to increasing RF congestion, as they are capable of being aware of the electromagnetic (EM) environment around them, adjust their operating parameters to maintain the maximum link performance, and learn their surrounding en- vironment across various conditions. A key performance metric in CR networks is in the accuracy and speed of gathering information about the available spectrum around them. In this dissertation, we introduce a full end-to-end CR node that uses an EM environmentally aware (EMEA) sensor, based on CS-based spectrum sensing hardware, to provide rapid wide-band frequency information to a machine learning based decision engine back-end. This Intelligent Transceiver Radio Node (ITRN) is capable spectrum sensing 2,000x faster than conventional methods which aides in a cre- ating a rapid response to fast moving frequency jammers. In addition, this dissertation introduces the Directional Spectrum Sensor (DSS) architecture that combines the benefits of beamforming with rapid CS-based spectrum sensing hardware. This results in the detection sensitivity improv- ing as large blockers are spatially filtered out.

This dissertation will also introduce a theoretical procedure using a dual mode sensing hardware that is capable of rapidly scanning the spectral and spatial domain. This provides future CR applications and integrations with the full picture of their surrounding environment in both the spatial and spectral domains. To further leverage the modulated-LO approach, the time-modulated LO (TM-LO) vector modulator (VM) architecture is introduced for beamforming receiver applications. Beamforming receivers use the spatial domain to increase sensitivity, reject spatial interferers, and increase communication to multiple users simultaneously with multiple beams. Typically the phase shifts and amplitude control are achieved by using conventional vector modulators (VM) consisting of multiple gain slices. The number of slices grows as a product of the number of beams, antenna elements, and bits of resolution. Thus resulting in significant area and power consumption, placing limitations on implementation for large arrays and potentially for CR applications.

Instead, we use time-modulation techniques to break the area and VM-resolution tradeoff. The TM-LO uses rail-to-rail LO waveforms generated from digitally synthesized blocks and pass-gate switches to perform the down-conversion and amplitude/phase control of the received signal. A single element receiver achieves 0.2 dB RMS gain error and 1.4◦RMS phase error with 5 bits of amplitude/phase resolution across a 360◦range is implemented in a 65 nm CMOS process as a proof-of-concept prototype. Without time-modulation, the hardware is capable of 3-bits of resolution. The inherent digital nature of TM-LO architecture provides opportunity very compact front-ends suitable for large arrays and lower voltage technologies. Four TM-LO chips were used to create a beamform- ing receiver that is capable of harmonic beamforming. The phased array implementation based on the TM-LO is presented and discussed.

In summary, several architectures are presented in this dissertion that use modulated LO waveforms to perform rapid state-of-the-art sensing capability to be integrated into an end-to-end CR platform and a new block level approach using the time-domain to create compact VMs for phased array systems. The system-level integration highlights the massive advantage of hardware insights that can be added to existing systems with low power, area, and integration costs. These systems can interface with new paradigms of signal processing and machine learning for massive performance improvement. The block level insights derived from the system-level work present new opportunties for circuit approaches that are tailored to the specific system requirements.