Bin Le

Bio: Bin Le is an academic researcher from Virginia Tech. The author has contributed to research in topics: Cognitive radio & Software-defined radio. The author has an hindex of 10, co-authored 11 publications receiving 790 citations. Previous affiliations of Bin Le include University of Virginia.

Analog-to-digital converters

Abstract: 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.

Network Support: The Radio Environment Map

Abstract: This chapter discusses the strategy of exploiting network support in cognitive radio (CR) systems architectures introducing the radio environment map (REM) as an innovative vehicle of providing network support to CRs. As a systematic top-down approach to providing network support to CRs, the radio environment map is proposed as an integrated database consisting of multi domain information such as geographical features, available services, spectral regulations, locations and activities of radios, policies of the user and/or service provider, and past experience. An radio environment map (REM) can be exploited by a CE to enhance or achieve most of cognitive functionalities such as SA, reasoning, learning, planning, and decision support. Leveraging both internal and external network support through global and local REMs presents a sensible approach to implementing CRs in a reliable, flexible, and cost effective way. Network support can dramatically relax the requirements on a CR device as well as improve the performance of the whole CR network. Considering the dynamic nature of spectral regulation and operation policy, the REM-based CR is flexible and future proof in the sense that it allows regulators or service providers to modify or change their rules or policies simply by updating REMs accordingly.

Cognitive radio applications to dynamic spectrum allocation: a discussion and an illustrative example

Abstract: In an effort to improve radio spectrum management and promote a more efficient use of it, regulatory bodies are currently trying to adopt a new spectrum access model. At the same time, cognitive radio technology has received a lot of interest as a possible enabling technology. In this paper, we provide a brief description of the broad impact of cognitive radios in different markets. At Virginia Tech's Center for Wireless Telecommunications (CWT), we have designed a biologically inspired cognitive engine with dynamic spectrum access (DSA) as one of its intended applications. An experimental software simulation shows a 20 dB SINR improvement using cognitive techniques in an interference environment over that provided by current IEEE 802.11a service PHY standard

Cognitive Radio: From Spectrum Sharing to Adaptive Learning and Reconfiguration

Abstract: This paper introduces important cognitive radio developments like spectrum sharing, learning and adaptation algorithms, and the software and hardware architecture to support these functions. A cognitive radio is defined here as a transceiver that is aware of its environment and can combine this awareness with knowledge of its user's priorities, needs, operational procedures, and governing regulatory rules. It adapts to its environment and configures itself in an appropriate fashion. The radio learns through experience and is capable of generating solutions for communications problems unforeseen by its designers. Our spectrum sharing cognitive radio is built upon GNU radio and uses the universal software radio peripheral (USRP) device as our radio front end platform. We use cyclostationary feature analysis to detect low SNR modulated signals because of its ability to distinguish between modulated signals, interference, and noise in low signal to noise ratios. A parallel algorithm running on a cell broadband engine (Cell BE) is used to attack the associated high computational complexity. A new spectrum sensing scheme, incorporating spectrum monitoring, data transmission, and dynamic channel switching, is designed to fully utilize the idle time of the primary user. Our work is based on the concept of a cognitive engine: an intelligent software package that "reads the meters" and "turns the knobs" of any attached software defined radio (SDR) platform. Using an eclectic combination of artificial intelligence techniques including case-based decision theory, multi-objective genetic algorithms, and neural networks, it implements a system of nested cognition loops. Applied to public safety communications, this technology is the basis of a working prototype Public Safety Cognitive Radio that can scan the public safety spectrum (multiple bands and multiple waveforms, all incompatible) and configure itself to interoperate with any public safety waveform that it finds within 0.1 seconds of determining that a signal is present.

Cognitive Radio Formulation and Implementation

Abstract: This paper approaches cognition on the physical and MAC layers by defining a common language of "knobs" and "meters" to discuss adaptation and learning. Cognitive radio merges artificial intelligence and software defined radios (SDR). It requires a simple language for communicating between these two levels. We define a method for doing this. We also discuss a genetic algorithm approach to perform intelligent radio adaptation, using the GNU Radio platform as an example. We provide both conceptual and practical implementation details of a cognitive radio acting at the physical and MAC layers. Results presented show the promise for the genetic algorithm adaptation within the multi-objective optimization environment of the cognitive radio.

An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

Abstract: 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.

Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

Abstract: 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.

Millimeter Wave Communications for Future Mobile Networks

Abstract: 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

MIMO Precoding and Combining Solutions for Millimeter-Wave Systems

Abstract: Millimeter-wave communication is one way to alleviate the spectrum gridlock at lower frequencies while simultaneously providing high-bandwidth communication channels. MmWave makes use of MIMO through large antenna arrays at both the base station and the mobile station to provide sufficient received signal power. This article explains how beamforming and precoding are different in MIMO mmWave systems than in their lower-frequency counterparts, due to different hardware constraints and channel characteristics. Two potential architectures are reviewed: hybrid analog/digital precoding/combining and combining with low-resolution analog- to-digital converters. The potential gains and design challenges for these strategies are discussed, and future research directions are highlighted.