https://www.cs.montana.edu/courses/csci566/Ref/CR-Survey.pdf

NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey

Communication at millimeter wave (mmWave) frequencies is defining a new era of wireless communication. The mmWave band offers higher bandwidth communication channels versus those presently used in commercial wireless systems. The applications of mmWave are immense: wireless local and personal area networks in the unlicensed band, 5G cellular systems, not to mention vehicular area networks, ad hoc networks, and wearables. Signal processing is critical for enabling the next generation of mmWave communication. Due to the use of large antenna arrays at the transmitter and receiver, combined with radio frequency and mixed signal power constraints, new multiple-input multiple-output (MIMO) communication signal processing techniques are needed. Because of the wide bandwidths, low complexity transceiver algorithms become important. There are opportunities to exploit techniques like compressed sensing for channel estimation and beamforming. This article provides an overview of signal processing challenges in mmWave wireless systems, with an emphasis on those faced by using MIMO communication at higher carrier frequencies.

An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

With the rapid deployment of new wireless devices and applications, the last decade has witnessed a growing demand for wireless radio spectrum. However, the fixed spectrum assignment policy becomes a bottleneck for more efficient spectrum utilization, under which a great portion of the licensed spectrum is severely under-utilized. The inefficient usage of the limited spectrum resources urges the spectrum regulatory bodies to review their policy and start to seek for innovative communication technology that can exploit the wireless spectrum in a more intelligent and flexible way. The concept of cognitive radio is proposed to address the issue of spectrum efficiency and has been receiving an increasing attention in recent years, since it equips wireless users the capability to optimally adapt their operating parameters according to the interactions with the surrounding radio environment. There have been many significant developments in the past few years on cognitive radios. This paper surveys recent advances in research related to cognitive radios. The fundamentals of cognitive radio technology, architecture of a cognitive radio network and its applications are first introduced. The existing works in spectrum sensing are reviewed, and important issues in dynamic spectrum allocation and sharing are investigated in detail.

/pdf/advances-in-cognitive-radio-networks-a-survey-3gp2nvo27m.pdf

Advances in cognitive radio networks: A survey

Wideband analog signals push contemporary analog-to-digital conversion (ADC) systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the band limit, although the locations of the frequencies may not be known a priori. For this type of sparse signal, other sampling strategies are possible. This paper describes a new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components. Let K denote the total number of frequencies in the signal, and let W denote its band limit in hertz. Simulations suggest that the random demodulator requires just O(K log(W/K)) samples per second to stably reconstruct the signal. This sampling rate is exponentially lower than the Nyquist rate of W hertz. In contrast to Nyquist sampling, one must use nonlinear methods, such as convex programming, to recover the signal from the samples taken by the random demodulator. This paper provides a detailed theoretical analysis of the system's performance that supports the empirical observations.

/pdf/beyond-nyquist-efficient-sampling-of-sparse-bandlimited-4l4hsdu9v4.pdf

Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond) We first summarize the recent channel measurement campaigns and modeling results Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed

Millimeter Wave Communications for Future Mobile Networks

This paper analyzed the internal relationships of the performance parameters of ADCs, showing their frequency dependency and structure dependency. The history and current trends in ADC technologies based on the P and F figures-of-merit were also reviewed. Historically, there was an increase in performance around 1994, with a share rise around 1997, which broke the stagnant performance discussed by Waiden (1999). While the past few years have shown a sharp increase in ADC performance, we have shown that performance and power dissipation depend greatly on the ADC structure and the target applications. With the progression of wideband radio systems like UWB and OFDM comes a growing demand to provide faster sampling rates and higher resolutions with lower power dissipation. With the innovation of advanced communication techniques like multi-input/multi-output and multistandard radios, the demand is growing to provide multichannel programmable data conversion, both of which are pushing the performance of ADCs further in the coming years.

/pdf/analog-to-digital-converters-4r1smhw9di.pdf

Analog-to-digital converters

We can think of a cognitive radio as having three b asic parts that make it cognitive: the ability to sense, inclu ding at a minimum sensing the RF spectrum, geographical surroundings, and the user’s needs; the capacity to learn, ideally in both supervised and unsupervised modes; and finally, the capability to adapt within any layer o f the radio communication system. At the Virginia Tech (VT) Center for Wireless Telecommunications (CWT), we have developed a cognitive radio engine to perform all o f these tasks. This paper presents the adaptive component, which uses genetic algorithms (GAs) to evolve a radio def ined by a chromosome. The chromosome’s genes represent the adjustable parameters in a given radio, and by gene tically manipulating the chromosomes, the GA can find a set of parameters that optimize the radio for the user’s c urrent needs. At the end of this paper, we present experim ental results on both a hardware platform and software si mulation.

Cognitive radios with genetic algorithms: intelligent control of software defined radios

We present a novel technique for radio transmitter identification based on frequency domain characteristics. Our technique detects the unique features imbued in a signal as it passes through a transmit chain. We are the first to propose the use of discriminatory classifiers based on steady state spectral features. In laboratory experiments, we achieve 97% accuracy at 30 dB SNR and 66% accuracy at OdB SNR based on eight identical universal software radio peripherals (USRP) transmitters. Our technique can be implemented using today's low cost high-volume receivers and requires no manual performance tuning.

/pdf/radio-transmitter-fingerprinting-a-steady-state-frequency-slorppe74f.pdf

Radio Transmitter Fingerprinting: A Steady State Frequency Domain Approach

Iris is a software architecture for building highly reconfigurable radio networks. It has formed the basis for a wide range of dynamic spectrum access and cognitive radio demonstration systems presented at a number of international conferences between 2007 and 2010. These systems have been developed using heterogeneous processing platforms including general-purpose processors, field-programmable gate arrays and the Cell Broadband Engine. Focusing on runtime reconfiguration, Iris offers support for all layers of the network stack and provides a platform for the development of not only reconfigurable point-to-point radio links but complete networks of cognitive radios. This article provides an overview of Iris, presenting the unique features of the architecture and illustrating how it can be used to develop a cognitive radio testbed.

Iris: an architecture for cognitive radio networking testbeds

This paper provides details of a distributed genetic algorithm (GA) based cognitive radio engine model for disaster communications and its implementation in a cognitive radio test bed using programmable radios. Future applications include tactical and covert communications. The cognitive system monitor (CSM) module presented here permits cross layer cognition and adaptation of a programmable radio by classifying the observed channel, matching channel behavior with operational goals, and passing these goals to a wireless system genetic algorithm (WSGA) adaptive controller module to evolve and optimize radio operation. The CSM module algorithm provides for parallel distributed operation and includes a learning classifier and meta-GA functions that work from a knowledge base (which may be distributed) in long term memory to synthesize matched channels and operational goals that are retained in short term memory. Experimental results show that the cognitive engine finds the best tradeoff between a host radio's operational parameters in changing wireless conditions, while the baseline adaptive controller only increases or decreases its data rate based on a threshold, often wasting usable bandwidth or excess power when it is not needed due its inability to learn.

Thomas W. Rondeau

Papers

Analog-to-digital converters

Cognitive radios with genetic algorithms: intelligent control of software defined radios

Radio Transmitter Fingerprinting: A Steady State Frequency Domain Approach

Iris: an architecture for cognitive radio networking testbeds

Cognitive radio testbed: further details and testing of a distributed genetic algorithm based cognitive engine for programmable radios