Robert C. Daniels

60 GHz wireless communications: emerging requirements and design recommendations

Multiple GHz of internationally available, unlicensed spectrum surrounding the 60 GHz carrier frequency has the ability to accommodate high-throughput wireless communications. While the size and availability of this free spectrum make it very attractive for wireless applications, 60 GHz implementations must overcome many challenges. For example, the high attenuation and directional nature of the 60 GHz wireless channel as well as limited gain amplifiers and excessive phase noise in 60 GHz transceivers are explicit implementation difficulties. The challenges associated with this channel motivate commercial deployment of short-range wireless local area networks, wireless personal area networks, and vehicular networks. In this paper we detail design tradeoffs for algorithms in the 60 GHz physical layer including modulation, equalization, and space-time processing. The discussion is enhanced by considering the limitations in circuit design, characteristics of the effective wireless channel (including antennas), and performance requirements to support current and next generation 60 GHz wireless communication applications.

60 GHz Wireless: Up Close and Personal

To meet the needs of next-generation high-data-rate applications, 60 GHz wireless networks must deliver Gb/s data rates and reliability at a low cost. In this article, we surveyed several ongoing challenges, including the design of cost-efficient and low-loss on-chip and in-package antennas and antenna arrays, the characterization of CMOS processes at millimeter-wave frequencies, the discovery of efficient modulation techniques that are suitable for the unique hardware impairments and frequency selective channel characteristics at millimeter-wave frequencies, and the creation of MAC protocols that more effectively coordinate 60 GHz networks with directional antennas. Solving these problems not only provides for wireless video streaming and interconnect replacement, but also moves printed and magnetic media such as books and hard drives to a lower cost, higher reliability semiconductor form factor with wireless connectivity between and within devices.

Early Results on Hydra: A Flexible MAC/PHY Multihop Testbed

Hydra is a flexible wireless network testbed being developed at UT Austin. Our focus is networks that support multiple wireless hops and where the network, especially the MAC, takes advantage of sophisticated PHY techniques, such as OFDM and MIMO. We argue that for this domain simulation alone is not adequate and that working prototypes are needed to validate algorithms and protocols. Hydra nodes consist of a flexible RF front-end and a general purpose machine with a software based MAC and PHY. Using the frameworks of the Click modular router and GNU radio and coding in C++ makes it relatively easy to implement working prototypes of cross-layer designs that require custom MACs and PHYs. We present the architecture and implementation of Hydra, as well as a preliminary cross-layer design experiment for a rate-adaptive MAC. These early results show Hydra is a capable prototyping tool for wireless network research.

Millimeter-Wave Vehicular Communication to Support Massive Automotive Sensing

As driving becomes more automated, vehicles are being equipped with more sensors generating even higher data rates. Radars are used for object detection, visual cameras as virtual mirrors, and LIDARs for generating high resolution depth associated range maps, all to enhance the safety and efficiency of driving. Connected vehicles can use wireless communication to exchange sensor data, allowing them to enlarge their sensing range and improve automated driving functions. Unfortunately, conventional technologies, such as DSRC and 4G cellular communication, do not support the gigabit-per-second data rates that would be required for raw sensor data exchange between vehicles. This article makes the case that mmWave communication is the only viable approach for high bandwidth connected vehicles. The motivations and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. A high-level solution to one key challenge - the overhead of mmWave beam training - is proposed. The critical feature of this solution is to leverage information derived from the sensors or DSRC as side information for the mmWave communication link configuration. Examples and simulation results show that the beam alignment overhead can be reduced by using position information obtained from DSRC.

Adaptation in Convolutionally Coded MIMO-OFDM Wireless Systems Through Supervised Learning and SNR Ordering

Multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems use link adaptation to exploit the dynamic nature of wireless environments. Link adaptation maximizes throughput while maintaining target reliability by adaptively selecting the modulation order and coding rate. Link adaptation is extremely challenging, however, due to the difficulty in predicting error rates in OFDM with binary convolutional codes, bit interleaving, MIMO processing, and real channel impairments. This paper proposes a new machine-learning framework that exploits past observations of the error rate and the associated channel-state information to predict the best modulation order and coding rate for new realizations of the channel state without modeling the input-output relationship of the wireless transceiver. Our approach is enabled through our new error-rate expression that is only parameterized by postprocessing signal-to-noise ratios (SNRs), ordered over subcarriers and spatial streams. Using ordered SNRs, we propose a low-dimensional feature set that enables machine learning to increase the accuracy of link adaptation. An IEEE 802.11n simulation study validates the application of this machine-learning framework in real channels and demonstrates the improved performance of SNR ordering as it compares with competing link-quality metrics.

An Experimental Evaluation of Rate Adaptation for Multi-Antenna Systems

Increasingly wireless networks use multi-antenna nodes as in IEEE 802.11n and 802.16. The Physical layer (PHY) in such systems may use the antennas to provide multiple streams of data (spatial multiplexing) or to increase the robustness of fewer streams. These physical layers also provide support for sending packets at different rates by changing the modulation and coding of transmissions. Rate adaptation is the problem of choosing the best transmission mode for the current channel and in these systems requires choosing both the level of spatial multiplexing and the modulation and coding. Hydra is an experimental wireless network node prototype in which both the MAC and PHY are highly programmable. Hydras PHY is essentially the 802.11n PHY, and currently supports two antennas and the same modulations and codings as 802.11n. Because of limitations of our hardware platform, the actual rates are a factor of 10 smaller than 802.11n. The MAC is essentially the 802.11 MAC with extensions, including the ability to feedback channel state or rate information from the receiver. Hydra was designed to allow experimentation with real radios, PHYs, and network stacks over real-world channels and it is well suited to studying rate adaptation in multi-antenna systems. To allow controlled experimentation, we also have the ability to perform experiments over emulated channels using exactly the same MAC and PHY used for RF transmissions. We present rate control experiments based on transmission over both real and emulated channels. Our experiments include measurements for single antenna systems and two antenna systems using a single or multiple spatial streams. We study rate adaptation algorithms using both explicit and implicit feedback from the receiver. A novel aspect of our results is the first experimental study of adaptation between single and multiple spatial streams for 802.11n style systems. Increasingly wireless networking technologies, including IEEE 802.11n and IEEE 802.16, support radios with multiple an- tennas. These antennas can be used to support multiple data streams (spatial multiplexing) or to increase robustness by tak- ing advantage of channel diversity (1), (2). Choosing between

An online learning framework for link adaptation in wireless networks

Current and future wireless networks require the selection of a plurality of parameters at different layers of the communication system to optimize network throughput while satisfying certain reliability constraints. In prior work, mathematical input/output models along with system performance expressions have been used to perform the parameter selection. In practice, however, impairments such as interference and analog circuit nonlinearities are difficult to model in a simple and tractable framework. Moreover, these impairments are in flux due to environmental factors. This paper summarizes an online machine learning approach to parameter selection through the real-time capturing of performance related data. Online learning is advantageous, not only because changes in the system model can be captured in the data observations, but also for its ability to learn system operation details not provided by current system models. A modified version of k-nearest neighbor is developed to enable both the real-time capture of training data and the performance criterion for physical layer adaptation.

Online adaptive modulation and coding with support vector machines

Optimizing the performance of adaptive modulation and coding (AMC) in practice has proven challenging. Prior research has struggled to find link quality metrics that are suitable for look-up-tables and simultaneously provide an injective mapping to error rate in wireless links that feature selective channels with hardware nonlinearities and non-Gaussian noise effects. This paper proposes a novel online support vector machine algorithm, compatible with accurate multidimensional link quality metrics, that is able to optimize AMC to the unique (potentially dynamic) hardware characteristics of each wireless device in selective channels. IEEE 802.11n simulations show that our proposed algorithm allows each individual wireless device to optimize the operating point in the rate/reliability tradeoff through frame-by-frame error evaluation. These simulations also show that our algorithm displays identical performance to alternative online AMC algorithms while drastically reducing complexity.

A Supervised Learning Approach to Adaptation in Practical MIMO-OFDM Wireless Systems

MIMO-OFDM wireless systems require adaptive modulation and coding based on channel state information (CSI) to maximize throughput in changing wireless channels. Traditional adaptive modulation and coding attempts to predict the best rate available by estimating the packet error rate for each modulation and coding scheme (MCS) by using CSI, which has shown to be challenging. This paper considers supervised learning with the k-nearest neighbor (k-NN) algorithm as a new framework for adaptive modulation and coding. Practical k-NN operation is enabled through feature space dimensionality reduction using subcarrier ordering techniques based on postprocessing SNR. Simulation results of an IEEE 802.11n draft-compatible physical layer in flat and frequency selective wireless channels shows the k-NN with an ordered subcarrier feature space performs near ideal adaptation under packet error rate constraints.

Link Adaptation with Position/Motion Information in Vehicle-to-Vehicle Networks

Wireless communication networks use link adaptation to select physical layer parameters that optimize the transmission strategy as a function of the wireless channel realization. In the vehicle-to-vehicle (V2V) networks considered in this letter, the short coherence time of the wireless channel makes link adaptation based on the impulse response challenging. Consequently, link adaptation in V2V wireless networks may instead exploit the large-scale characteristics of the wireless channel (i.e. path loss) since they evolve slowly and enable less frequent feedback. Large-scale channel information may be captured through channel or position/motion measurements. We show, through the definition of new large-scale coherence expressions, that channel measurements render large-scale coherence as a function of time-change while the position/motion measurements render coherence as a function of velocity-change. This letter is concluded with highway simulations of modeled and measured channels to demonstrate the advantage of position/motion information for feedback reduction in V2V link adaptation.