Enhancing Spectrum Sensing via Reconfigurable Intelligent Surfaces: Passive or Active Sensing and How Many Reflecting Elements are Needed?

Cognitive radio has been proposed to alleviate the scarcity of available spectrum caused by the significant demand for wideband services and the fragmentation of spectrum resources. However, sensing performance is quite poor due to the low sensing signal-to-noise ratio, especially in complex environments with severe channel fading. Fortunately, reconfigurable intelligent surface (RIS)-aided spectrum sensing can effectively tackle the above challenge due to its high array gain. Nevertheless, the traditional passive RIS may suffer from the ``double fading'' effect, which severely limits the performance of passive RIS-aided spectrum sensing. Thus, a crucial challenge is how to fully exploit the potential advantages of the RIS and further improve the sensing performance. To this end, we introduce the active RIS into spectrum sensing and respectively formulate two optimization problems for the passive RIS and the active RIS to maximize the detection probability. In light of the intractability of the formulated problems, we develop a one-stage optimization algorithm with inner approximation and a two-stage optimization algorithm with a bisection method to obtain sub-optimal solutions, and apply the Rayleigh quotient to obtain the upper and lower bounds of the detection probability. Furthermore, in order to gain more insight into the impact of the RIS on spectrum sensing, we respectively investigate the number configuration for passive RIS and active RIS and analyze how many reflecting elements are needed to achieve the detection probability close to 1. Simulation results verify that the proposed algorithms outperform existing algorithms under the same parameter configuration, and achieve a detection probability close to 1 with even fewer reflecting elements or antennas than existing schemes

A Case Study in Physical-Layer Steganography Applied to Multicarrier Transmissions

Covert communications can be a force for good, such as providing a means of message authentication to prevent malicious actors from spoofing networks. This dissertation explores the design of a covert signal to be hidden inside the bandwidth of an Orthogonal Frequency Division Multiplexing (OFDM) signal. In order to make detection by unintended observers as difficult as possible, the covert signal operates as interference inside the OFDM signal and is set to a high Signal to Interference Ratio (SIR). Given the high SIR, the OFDM signal must be cancelled in order to recover the covert signal. The detectability of the covert signal is tested using multiple detectors with and without cancellation. Among the detectors used is a Convolutional Neural Network (CNN) designed for image classification that has been repurposed through transfer learning to detect signal activity in noise and interference. The CNN detector demonstrates resilience in the presence of narrowband interference. The cancellation algorithm is enhanced with an estimate of OFDM windowing as applied at the transmitter, which is an often-overlooked parameter in cancellation applications. The enhanced cancellation-algorithm improves the cancellation of OFDM signals by 5.3 dB in an over-the-air test. The enhanced cancellation-algorithm also improves the Packet Error Rate of OFDM signals and improves the recovery of the covert signal. The improved recovery has direct application to Power-Domain Non-orthogonal Multiple Access and Rate-Splitting Multiple Access, which both rely on successive interference cancellation. Lastly, to frustrate any efforts to analyze the covert waveform, the covert signal is augmented with an adversarial waveform designed to exploit weaknesses in CNNs used for modulation classification. The classification system suffers from uncertainty in the bandwidth estimate of the covert signal. The system will likely err on the side of making the bandwidth wider than necessary. It is demonstrated that a wider bandwidth makes the attack more successful, as opposed to other estimation errors which prior literature has shown to weaken the effectiveness of these attacks

Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions

Reconfigurable Receiver Front-Ends for Advanced Telecommunication Technologies

The exponential growth of converging technologies, including augmented reality, autonomous vehicles, machine-to-machine and machine-to-human interactions, biomedical and environmental sensory systems, and artificial intelligence, is driving the need for robust infrastructural systems capable of handling vast data volumes between end users and service providers. This demand has prompted a significant evolution in wireless communication, with 5G and subsequent generations requiring exponentially improved spectral and energy efficiency compared to their predecessors. Achieving this entails intricate strategies such as advanced digital modulations, broader channel bandwidths, complex spectrum sharing, and carrier aggregation scenarios. A particularly challenging aspect arises in the form of non-contiguous aggregation of up to six carrier components across the frequency range 1 (FR1). This necessitates receiver front-ends to effectively reject out-of-band (OOB) interferences while maintaining high-performance in-band (IB) operation. Reconfigurability becomes pivotal in such dynamic environments, where frequency resource allocation, signal strength, and interference levels continuously change. Software-defined radios (SDRs) and cognitive radios (CRs) emerge as solutions, with direct RF-sampling receivers offering a suitable architecture in which the frequency translation is entirely performed in digital domain to avoid analog mixing issues. Moreover, direct RF- sampling receivers facilitate spectrum observation, which is crucial to identify free zones, and detect interferences. Acoustic and distributed filters offer impressive dynamic range and sharp roll off characteristics, but their bulkiness and lack of electronic adjustment capabilities limit their practicality. Active filters, on the other hand, present opportunities for integration in advanced CMOS technology, addressing size constraints and providing versatile programmability. However, concerns about power consumption, noise generation, and linearity in active filters require careful consideration.This thesis primarily focuses on the design and implementation of a low-voltage, low-power RFFE tailored for direct sampling receivers in 5G FR1 applications. The RFFE consists of a balun low-noise amplifier (LNA), a Q-enhanced filter, and a programmable gain amplifier (PGA). The balun-LNA employs noise cancellation, current reuse, and gm boosting for wideband gain and input impedance matching. Leveraging FD-SOI technology allows for programmable gain and linearity via body biasing. The LNA's operational state ranges between high-performance and high-tolerance modes, which are apt for sensitivityand blocking tests, respectively. The Q-enhanced filter adopts noise-cancelling, current-reuse, and programmable Gm-cells to realize a fourth-order response using two resonators. The fourth-order filter response is achieved by subtracting the individual response of these resonators. Compared to cascaded and magnetically coupled fourth-order filters, this technique maintains the large dynamic range of second-order resonators. Fabricated in 22-nm FD-SOI technology, the RFFE achieves 1%-40% fractional bandwidth (FBW) adjustability from 1.7 GHz to 6.4 GHz, 4.6 dB noise figure (NF) and an OOB third-order intermodulation intercept point (IIP3) of 22 dBm. Furthermore, concerning the implementation uncertainties and potential variations of temperature and supply voltage, design margins have been considered and a hybrid calibration scheme is introduced. A combination of on-chip and off-chip calibration based on noise response is employed to effectively adjust the quality factors, Gm-cells, and resonance frequencies, ensuring desired bandpass response. To optimize and accelerate the calibration process, a reinforcement learning (RL) agent is used.Anticipating future trends, the concept of the Q-enhanced filter extends to a multiple-mode filter for 6G upper mid-band applications. Covering the frequency range from 8 to 20 GHz, this RFFE can be configured as a fourth-order dual-band filter, two bandpass filters (BPFs) with an OOB notch, or a BPF with an IB notch. In cognitive radios, the filter’s transmission zeros can be positioned with respect to the carrier frequencies of interfering signals to yield over 50 dB blocker rejection

Nonlinear Distortion in Wideband Radio Receivers and Analog-to-Digital Converters: Modeling and Digital Suppression

Emerging wireless communications systems aim to flexible and efficient usage of radio spectrum in order to increase data rates. The ultimate goal in this field is a cognitive radio. It employs spectrum sensing in order to locate spatially and temporally vacant spectrum chunks that can be used for communications. In order to achieve that, flexible and reconfigurable transceivers are needed. A software-defined radio can provide these features by having a highly-integrated wideband transceiver with minimum analog components and mostly relying on digital signal processing. This is also desired from size, cost, and power consumption point of view. However, several challenges arise, from which dynamic range is one of the most important. This is especially true on receiver side where several signals can be received simultaneously through a single receiver chain. In extreme cases the weakest signal can be almost 100 dB weaker than the strongest one. Due to the limited dynamic range of the receiver, the strongest signals may cause nonlinear distortion which deteriorates spectrum sensing capabilities and also reception of the weakest signals. The nonlinearities are stemming from the analog receiver components and also from analog-to-digital converters (ADCs). This is a performance bottleneck in many wideband communications and also radar receivers. The dynamic range challenges are already encountered in current devices, such as in wideband multi-operator receiver scenarios in mobile networks, and the challenges will have even more essential role in the future.This thesis focuses on aforementioned receiver scenarios and contributes to modeling and digital suppression of nonlinear distortion. A behavioral model for direct-conversion receiver nonlinearities is derived and it jointly takes into account RF, mixer, and baseband nonlinearities together with I/Q imbalance. The model is then exploited in suppression of receiver nonlinearities. The considered method is based on adaptive digital post-processing and does not require any analog hardware modification. It is able to extract all the necessary information directly from the received waveform in order to suppress the nonlinear distortion caused by the strongest blocker signals inside the reception band.In addition, the nonlinearities of ADCs are considered. Even if the dynamic range of the analog receiver components is not limiting the performance, ADCs may cause considerable amount of nonlinear distortion. It can originate, e.g., from undeliberate variations of quantization levels. Furthermore, the received waveform may exceed the nominal voltage range of the ADC due to signal power variations. This causes unintentional signal clipping which creates severe nonlinear distortion. In this thesis, a Fourier series based model is derived for the signal clipping caused by ADCs. Furthermore, four different methods are considered for suppressing ADC nonlinearities, especially unintentional signal clipping. The methods exploit polynomial modeling, interpolation, or symbol decisions for suppressing the distortion. The common factor is that all the methods are based on digital post-processing and are able to continuously adapt to variations in the received waveform and in the receiver itself. This is a very important aspect in wideband receivers, especially in cognitive radios, when the flexibility and state-of-the-art performance is required

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems