Hun-Seok Kim

Energy-Constrained Link Adaptation for MIMO OFDM Wireless Communication Systems

We present a link adaptation strategy for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based wireless communications. Our objective is to choose the optimal mode that will maximize energy efficiency or data throughput subject to a given quality of service (QoS) constraint. We formulate the link adaptation problem as a convex optimization problem and expand the set of parameters under the control of the link adaptation protocol to include: number of spatial streams, number of transmit/receive antennas, use of spatial multiplexing or space time block coding (STBC), constellation size, bandwidth, transmit power and choice of maximum likelihood (ML) or zero-forcing (ZF) for MIMO decoding. Additionally, we increase the fidelity of the energy consumption modeling relative to the prior art. The resulting solution allows us to easily and quickly search the space of possible system parameters to deliver on the QoS with minimal energy consumption. Moreover, it provides us insight into where crossovers occur in the choice of the radio parameters. Application of the results to a generic MIMO-OFDM radio shows that the proposed strategy can provide an order of magnitude improvement in energy efficiency or data throughput relative to a static strategy.

A practical, hardware friendly MMSE detector for MIMO-OFDM-based systems

Design and implementation of a highly optimized MIMO (multiple-input multiple-output) detector requires cooptimization of the algorithm with the underlying hardware architecture. Special attention must be paid to application requirements such as throughput, latency, and resource constraints. In this work, we focus on a highly optimized matrix inversion free MMSE (minimum mean square error) MIMO detector implementation. The work has resulted in a real-time field-programmable gate array-based implementation (FPGA-) on a Xilinx Virtex-2 6000 using only 9003 logic slices, 66 multipliers, and 24 Block RAMs (less than 33% of the overall resources of this part). The design delivers over 420 Mbps sustained throughput with a small 2.77-microsecond latency. The designed linear MMSE MIMO detector is capable of complying with the proposed IEEE 802.11n standard.

A real time MIMO OFDM testbed for cognitive radio & networking research

A real time, 2 Mbps to 200 Mbps portable radio unit with MIMO and sensing capability which exposes all the PHY parameters to the higher layers will help advance experimental cognitive radio (CR), and wireless networking research. Collaboration between Silvus Communication Systems and the UCLA WISR group has resulted in the first generation radio specifically designed to meet the needs of the CR and wireless networking community. The current platform is based on a COTS FPGA platform with dual-band RF capabilities. It implements a slight variant of the 802.11n draft spec. It is a fully self contained PHY solution with over 100 unique modes of operation. Moreover it features a robust API to the MAC through which all PHY parameters can be controlled on a per-packet basis. The same API will allow the PHY to communicate channel state information, SNR, and RSSI measurements to the MAC. A 16 micro-second packet decode latency ensures that the PHY processing does not inhibit the systems fast response to changing channel and interference condition.

Energy-aware link adaptation for MIMO-OFDM based wireless communication

Multi-input multi-output (MIMO) antenna systems provide the user with additional degrees of freedom over traditional single antenna (SISO) system to enable optimum transmission. This paper focuses on quantifying the maximum energy efficiency of ldquomode-richrdquo MIMO wireless communication systems. We consider a MIMO-OFDM (orthogonal frequency division multiplexing) based software defined radio (SDR) where the user is allowed to dynamically change various system parameters such as bandwidth, transmit power, constellation size, channel coding rate, decoding algorithm, and MIMO antenna configurations. Based on these parameters, standard geometric programming problems are formulated to optimize energy efficiency of the system given quality of service (QoS) constraints. The result is a comprehensive MIMO link adaptation strategy that can optimally match the communication mode to the user QoS requirement and the channel condition. Results show that an optimized MIMO system can provide an order of magnitude improvement in energy efficiency over a static strategy.

A low power software-defined-radio baseband processor for the Internet of Things

In this paper, we define a configurable Software Defined Radio (SDR) baseband processor design for the Internet of Things (IoT). We analyzed the fundamental algorithms in communications systems on IoT devices to enable a microarchitecture design that supports many IoT standards and custom nonstandard communications. Based on this analysis, we propose a custom SIMD execution model coupled with a scalar unit. We introduce several architectural optimizations to this design: streaming registers, variable bit width datapath, dedicated ALUs for critical kernels, and an optimized flexible reduction network. We employ voltage scaling and clock gating to further reduce the power, while more than a 100% time margin has been reserved for reliable operation in the near-threshold region. Together our architectural enhancements lead to a 71× power reduction compared to a classic general purpose SDR SIMD architecture. Our IoT SDR datapath has sub-mW power consumption based on SPICE simulation, and is placed and routed to fit within an area of 0.074mm2 in a 28nm process. We implemented several essential elementary signal processing kernels and combined them to demonstrate two end-to-end upper bound systems, 802.15.4-OQPSK and Bluetooth Low Energy. Our full SDR baseband system consists of a configurable SIMD with a control plane MCU and memory. For comparison, the best commercial wireless transceiver consumes 23.8mW for the entire wireless system (digital/RF/ analog). We show that our digital system power is below 2mW, in other words only 8% of the total system power. The wireless system is dominated by RF/analog power comsumption, thus the price of flexibility that SDR affords is small. We believe this work is unique in demonstrating the value of baseband SDR in the low power IoT domain.

Energy-Autonomous Wireless Communication for Millimeter-Scale Internet-of-Things Sensor Nodes

This paper presents an energy-autonomous wireless communication system for ultra-small Internet-of-Things (IoT) platforms. In the proposed system, all necessary components, including the battery, energy-harvesting solar cells, and the RF antenna, are fully integrated within a millimeter-scale form factor. Designing an energy-optimized wireless communication system for such a miniaturized platform is challenging because of unique system constraints imposed by the ultra-small system dimension. The proposed system targets orders of magnitude improvement in wireless communication energy efficiency through a comprehensive system-level analysis that jointly optimizes various system parameters, such as node dimension, modulation scheme, synchronization protocol, RF/analog/digital circuit specifications, carrier frequency, and a miniaturized 3-D antenna. We propose a new protocol and modulation schemes that are specifically designed for energy-scarce ultra-small IoT nodes. These new schemes exploit abundant signal processing resources on gateway devices to simplify design for energy-scarce ultra-small sensor nodes. The proposed dynamic link adaptation guarantees that the ultra-small IoT node always operates in the most energy efficient mode for a given operating scenario. The outcome is a truly energy-optimized wireless communication system to enable various classes of new applications, such as implanted smart-dust devices.

14.7 A 288µW programmable deep-learning processor with 270KB on-chip weight storage using non-uniform memory hierarchy for mobile intelligence

Deep learning has proven to be a powerful tool for a wide range of applications, such as speech recognition and object detection, among others. Recently there has been increased interest in deep learning for mobile IoT [1] to enable intelligence at the edge and shield the cloud from a deluge of data by only forwarding meaningful events. This hierarchical intelligence thereby enhances radio bandwidth and power efficiency by trading-off computation and communication at edge devices. Since many mobile applications are “always-on” (e.g., voice commands), low power is a critical design constraint. However, prior works have focused on high performance reconfigurable processors [2–3] optimized for large-scale deep neural networks (DNNs) that consume >50mW. Off-chip weight storage in DRAM is also common in the prior works [2–3], which implies significant additional power consumption due to intensive off-chip data movement.

A fixed-point neural network for keyword detection on resource constrained hardware

Keyword detection is typically used as a front-end to trigger automatic speech recognition and spoken dialog systems. The detection engine needs to be continuously listening, which has strong implications on power and memory consumption. In this paper, we devise a neural network architecture for keyword detection and present a set of techniques for reducing the memory requirements in order to make the architecture suitable for resource constrained hardware. Specifically, a fixed-point implementation is considered; aggressively scaling down the precision of the weights lowers the memory compared to a naive floating-point implementation. For further optimization, a node pruning technique is proposed to identify and remove the least active nodes in a neural network. Experiments are conducted over 10 keywords selected from the Resource Management (RM) database. The trade-off between detection performance and memory is assessed for different weight representations. We show that a neural network with as few as 5 bits per weight yields a marginal and acceptable loss in performance, while requiring only 200 kilobytes (KB) of on-board memory and a latency of 150 ms. A hardware architecture using a single multiplier and a power consumption of less than 10mW is also presented.

MIMO Systems for Military Communications

Since the seminal work of Foschini, Gans and Teletar, the research community has generated a large body of work dealing with various aspects and benefits of MIMO for wireless data communications in civilian and commercial systems. Here we focus on the use of MIMO for military communication. In particular the effectiveness of MIMO for: (a) communications under very high mobility such as UAV based communication and, (b) the use of multi antenna techniques for covert (LPD) communications. Our results show the ability to operate at speeds of up to 200 mph, and a 17 dB reduction in the required TX power for covert, LPD communications, in addition to an interference/jammer mitigation technique based on MIMO eigen beam-nulling

Human body and head characteristics as a communication medium for Body Area Network

An in-depth investigation of the Body Channel Communication (BCC) under the environment set according to the IEEE 802.15.6 Body Area Network (BAN) standard is conducted to observe and characterize the human body as a communication medium. A thorough measurement of the human head as part of the human channel is also carried out. Human forehead, head to limb, and ear to ear channel is characterized. The channel gain of the human head follows the same bandpass profile of the human torso and limbs with the maximum channel gain occurring at 35MHz. The human body channel gain distribution histogram at given frequencies, while all the other parameters are held constant, exhibits a maximum variation of 2.2dB in the channel gain at the center frequency of the bandpass channel gain profile.