R-3 Repository :: Browsing by Author "Wu, Michael"

Browsing by Author "Wu, Michael"

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Architectures for Cognitive Radio Testbeds and Demonstrators – An Overview
(IEEE, 2010-06-01) Gustafsson, Oscar; Amiri, Kiarash; Andersson, Dennis; Blad, Anton; Bonner, Christian; Cavallaro, Joseph R.; Declerck, Jeroen; Dejonghe, Antoine; Eliardsson, Patrik; Glasse, Miguel; Hayar, Aawatif; Hollevoet, Lieven; Hunter, Chris; Joshi, Madhura; Kaltenberger, Florian; Knopp, Raymond; Le, Khanh; Miljanic, Zoran; Murphy, Patrick; Naessens, Frederik; Nikaein, Navid; Nussbaum, Dominique; Pacalet, Renaud; Raghavan, Praveen; Sabharwal, Ashutosh; Sarode, Onkar; Spasojevic, Predrag; Sun, Yang; Tullberg, Hugo M.; Vander Aa, Tom; Van der Perre, Liesbet; Wetterwald, Michelle; Wu, Michael; Center for Multimedia Communication
Wireless communication standards are developed at an ever-increasing rate of pace, and significant amounts of effort is put into research for new communication methods and concepts. On the physical layer, such topics include MIMO, cooperative communication, and error control coding, whereas research on the medium access layer includes link control, network topology, and cognitive radio. At the same time, implementations are moving from traditional fixed hardware architectures towards software, allowing more efficient development. Today, field-programmable gate arrays (FPGAs) and regular desktop computers are fast enough to handle complete baseband processing chains, and there are several platforms, both open-source and commercial, providing such solutions. The aims of this paper is to give an overview of five of the available platforms and their characteristics, and compare the features and performance measures of the different systems.
Baseband Signal Compression in Wireless Base Stations
(IEEE, 2012-12-01) Vosoughi, Aida; Wu, Michael; Cavallaro, Joseph R.; CMC
To comply with the evolving wireless standards, base stations must provide greater data rates over the serial data link between base station processor and RF unit. This link is especially important in distributed antenna systems and cooperating base stations settings. This paper explores the compression of baseband signal samples prior to transfer over the above-mentioned link. We study lossy and lossless compression of baseband signals and analyze the cost and gain of each approach. Sample quantizing is proposed as a lossy compression scheme and it is shown to be effective by experiments. With QPSK modulation, sample quantizing achieves a compression ratio of 4:1 and 3.5:1 in downlink and uplink, respectively. The corresponding compression ratios are 2.3:1 and 2:1 for 16-QAM. In addition, lossless compression algorithms including arithmetic coding, Elias-gamma coding, and unused significant bit removal, and also a recently proposed baseband signal compression scheme are evaluated. The best compression ratio achieved for lossless compression is 1.5:1 in downlink. Our over-the-air experiments suggest that compression of baseband signal samples is a feasible and promising solution for increasing the effective bit rates of the link to/from remote RF units without requiring much complexity and cost to the base station.
Cooperative Partial Detection Using MIMO Relays
(IEEE, 2011-10-01) Amiri, Kiarash; Wu, Michael; Cavallaro, Joseph R.; Lilleberg, Jorma; Center for Multimedia Communication
Using multiple-input multiple-output (MIMO) relays in cooperative communication improves the data rate and reliability of the communication. The MIMO transmission, however, requires considerable resources for the detection in the relay. In particular, if a full detect-and-forward (FDF) strategy is employed, the relay needs to spend considerable resources to perform the full MIMO detection. We propose a novel cooperative partial detection (CPD) strategy to partition the detection task between the relay and the destination. CPD modifies the tree traversal of the tree-based sphere detectors in a way where there is no need to visit all the levels of the tree and only a subset of the levels; thus, a subset of the transmitted streams are visited. The destination, then, combines the source signal and the partial relay signal to perform the final detection step and recover the transmitted vector. We study and compare the performance and complexity of FDF and CPD and show that by using the CPD approach, the relay can avoid the considerable overhead of MIMO detection while helping the source-destination link to improve its performance. More specifically, in the case of a 4X4 system, the relay complexity can be reduced by up to 80% of the conventional relaying scheme.
Data Compression in Base Transceiver Systems
(2012-10) Vosoughi, Aida; Cavallaro, Joseph R.; Wu, Michael
Efficient Detectors for LTE Uplink Systems: From Small to Large Systems
(2016-10-25) Wu, Michael; Cavallaro, Joseph R
3GPP Long Term Evolution (LTE) is currently the most popular cellular wireless communication standard. Future releases of the 3GPP specifications consider large-scale (or massive) multiple-input multiple-output(MIMO), an emerging technology where the base station (BS) is equipped with hundreds of antennas. Although large-scale MIMO improves spectral efficiency, link reliability, and coverage over conventional (small-scale) MIMO systems, the dimensionality of large-scale systems increases the computational complexity of uplink data detection significantly. I present efficient data detection algorithms for the LTE uplink and analyze the performance-complexity tradeoff for small to large-scale multiple-input multiple-output (MIMO) systems. I propose an iterative detection and decoding (IDD) scheme which combines frequency domain minimum mean-square error (FD-MMSE) equalization with parallel interference cancellation (PIC) to achieve near-optimal performance and show this scheme achieves near-optimal detection performance if the number of BS antennas exceeds the number of users by roughly 2x. For (symmetric) small-scale MIMO systems, IDD significantly reduces the frame error rate (FER) while the gains with large-scale MIMO are comparably smaller, which suggests MMSE detection is sufficient for large-scale MIMO systems. Linear MMSE detection still requires a computationally complex matrix inversion. For systems with very large ratios between the number of BS and user antennas, matrix inversion is performed on a strongly diagonally dominant matrix. I investigate a variety of exact and approximate equalization schemes that solve the system of linear equations either explicitly (requiring the computation of a matrix inverse) or implicitly (by directly computing the solution vector), and we analyze the associated performance/complexity trade-offs. I show that for small base-station (BS)-to-user-antenna ratios, exact and implicit data detection using the Cholesky decomposition achieves near-optimal performance at low complexity; for large BS-to-user-antenna ratios, implicit data detection using approximate equalization methods results in the best trade-off. Finally, I show by combining the advantages of exact, approximate, implicit, and explicit matrix inversion, I develop a new frequency-adaptive equalizer (FADE), which outperforms existing linear data-detection methods in terms of performance and complexity and can scale from small-scale MIMO systems to large-scale MIMO systems.
A Fast and Efficient Sift Detector Using The Mobile GPU
(IEEE, 2013-06) Rister, Blaine; Wang, Guohui; Wu, Michael; Cavallaro, Joseph R.
Emerging mobile applications, such as augmented reality, demand robust feature detection at high frame rates. We present an implementation of the popular Scale-Invariant Feature Transform (SIFT) feature detection algorithm that incorporates the powerful graphics processing unit (GPU) in mobile devices. Where the usual GPU methods are inefficient on mobile hardware, we propose a heterogeneous dataflow scheme. By methodically partitioning the computation, compressing the data for memory transfers, and taking into account the unique challenges that arise out of the mobile GPU, we are able to achieve a speedup of 4-7x over an optimized CPU version, and a 6.4x speedup over a published GPU implementation. Additionally, we reduce energy consumption by 87 percent per image. We achieve near-realtime detection without compromising the original algorithm.
Flexible N-Way MIMO Detector on GPU
(IEEE Computer Society, 2012-10-17) Wu, Michael; Yin, Bei; Cavallaro, Joseph R.; CMC
This paper proposes a flexible Multiple-Input Multiple-Output (MIMO) detector on graphics processing units (GPU). MIMO detection is a key technology in broadband wireless system such as LTE,WiMAX, and 802.11n. Existing detectors either use costly sorting for better performance or sacrifice sorting for higher throughput. To achieve good performance with high thoughput, our detector runs multiple search passes in parallel, where each search pass detects the transmit stream with a different permuted detection order. We show that this flexible detector, including QR decomposition preprocessing, outperforms existing GPU MIMO detectors while maintaining good bit error rate (BER) performance. In addition, this detector can achieve different tradeoffs between throughput and accuracy by changing the number of parallel search passes.
FPGA Prototyping of A High Data Rate LTE Uplink Baseband Receiver
(IEEE, 2009-11-01) Wang, Guohui; Yin, Bei; Amiri, Kiarash; Sun, Yang; Wu, Michael; Cavallaro, Joseph R.; Center for Multimedia Communication
The Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) standard is becoming the appropriate choice to pave the way for the next generation wireless and cellular standards. While the popular OFDM technique has been adopted and implemented in previous standards and also in the LTE downlink, it suffers from high peak-to-average-power ratio (PAPR). High PAPR requires more sophisticated power amplifiers (PAs) in the handsets and would result in lower efficiency PAs. In order to combat such effects, the LTE uplink choice of transmission is the novel Single Carrier Frequency Division Multiple Access (SC-FDMA) scheme which has lower PAPR due to its inherent signal structure. While reducing the PAPR, the SC-FDMA requires a more complicated detector structure in the base station for multi-antenna and multi-user scenarios. Since the multi-antenna and multi-user scenarios are critical parts of the LTE standard to deliver high performance and data rate, it is important to design novel architectures to ensure high reliability and data rate in the receiver. In this paper, we propose a flexible architecture of a high data rate LTE uplink receiver with multiple receive antennas and implemented a single FPGA prototype of this architecture. The architecture is verified on the WARPLab (a software defined radio platform based on Rice Wireless Open-access Research Platform) and tested in the real over-the-air indoor channel.
GPU Accelerated Scalable Parallel Decoding of LDPC Codes
(IEEE, 2011-11-01) Wang, Guohui; Wu, Michael; Sun, Yang; Center for Multimedia Communication
This paper proposes a flexible low-density parity-check (LDPC) decoder which leverages graphic processor units (GPU) to provide high decoding throughput. LDPC codes are widely adopted by the new emerging standards for wireless communication systems and storage applications due to their near-capacity error correcting performance. To achieve high decoding throughput on GPU, we leverage the parallelism embedded in the check-node computation and variable-node computation and propose a parallel strategy of partitioning the decoding jobs among multi-processors in GPU. In addition, we propose a scalable multi-codeword decoding scheme to fully utilize the computation resources of GPU. Furthermore, we developed a novel adaptive performance-tuning method to make our decoder implementation more flexible and scalable. The experimental results show that our LDPC decoder is scalable and flexible, and the adaptive performance-tuning method can deliver the peak performance based on the GPU architecture.
A GPU Implementation of a Real-Time MIMO Detector
(IEEE, 2009-10-01) Wu, Michael; Gupta, Siddharth; Sun, Yang; Cavallaro, Joseph R.; Center for Multimedia Communication
Multiple-input multiple-output (MIMO) is an existing technique that can significantly increase throughput of the system by employing multiple antennas at the transmitter and the receiver. Realizing maximum benefit from this technique requires computationally intensive detectors which poses significant challenges to receiver design. Furthermore, a flexible detector or multiple detectors are needed to handle different configurations. Graphical Processor Unit (GPU), a highly parallel commodity programmable co-processor, can deliver extremely high computation throughput and is well suited for signal processing applications. However, careful architecture aware design is needed to leverage performance offered by GPU. We show we can achieve good performance while maintaining flexibility by employing an optimized trellis-based MIMO detector on GPU.
Hemichordate genomes and deuterostome origins
(Macmillan Publishers Limited, 2015) Simakov, Oleg; Kawashima, Takeshi; Marlétaz, Ferdinand; Jenkins, Jerry; Koyanagi, Ryo; Mitros, Therese; Hisata, Kanako; Bredeson, Jessen; Shoguchi, Eiichi; Gyoja, Fuki; Yue, Jia-Xing; Chen, Yi-Chih; Freeman, Robert M.; Sasaki, Akane; Hikosaka-Katayama, Tomoe; Sato, Atsuko; Fujie, Manabu; Baughman, Kenneth W.; Levine, Judith; Gonzalez, Paul; Cameron, Christopher; Fritzenwanker, Jens H.; Pani, Ariel M.; Goto, Hiroki; Kanda, Miyuki; Arakaki, Nana; Yamasaki, Shinichi; Qu, Jiaxin; Cree, Andrew; Ding, Yan; Dinh, Huyen H.; Dugan, Shannon; Holder, Michael; Jhangiani, Shalini N.; Kovar, Christie L.; Lee, Sandra L.; Lewis, Lora R.; Morton, Donna; Nazareth, Lynne V.; Okwuonu, Geoffrey; Santibanez, Jireh; Chen, Rui; Richards, Stephen; Muzny, Donna M.; Gillis, Andrew; Peshkin, Leonid; Wu, Michael; Humphreys, Tom; Su, Yi-Hsien; Putnam, Nicholas H.; Schmutz, Jeremy; Fujiyama, Asao; Yu, Jr-Kai; Tagawa, Kunifumi; Worley, Kim C.; Gibbs, Richard A.; Kirschner, Marc W.; Lowe, Christopher J.; Satoh, Noriyuki; Rokhsar, Daniel S.; Gerhart, John
Acorn worms, also known as enteropneust (literally, ‘gut-breathing’) hemichordates, are marine invertebrates that share features with echinoderms and chordates. Together, these three phyla comprise the deuterostomes. Here we report the draft genome sequen
High-Throughput Data Detection for Massive MU-MIMO-OFDM Using Coordinate Descent
(IEEE, 2016) Wu, Michael; Dick, Chris; Cavallaro, Joseph R.; Studer, Christoph
Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8-user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than 2.6× in terms of throughput per FPGA look-up tables.
Implementation of a 3GPP LTE Turbo Decoder Accelerator on GPU
(IEEE, 2010-10-01) Wu, Michael; Cavallaro, Joseph R.; Center for Multimedia Communication
This paper presents a 3GPP LTE compliant turbo decoder accelerator on GPU. The challenge of implementing a turbo decoder is finding an efficient mapping of the decoder algorithm on GPU, e.g. finding a good way to parallelize workload across cores and allocate and use fast on-die memory to improve throughput. In our implementation, we increase throughput through 1) distributing the decoding workload for a codeword across multiple cores, 2) decoding multiple codewords simultaneously to increase concurrency and 3) employing memory optimization techniques to reduce memory bandwidth requirements. In addition, we analyze how different MAP algorithm approximations affect both throughput and bit error rate (BER) performance of this decoder.
Implementation of a High Throughput 3GPP Turbo Decoder on GPU
(Springer, 2011-11-01) Wu, Michael; Sun, Yang; Wang, Guohui; Cavallaro, Joseph R.; Center for Multimedia Communication
Turbo code is a computationally intensive channel code that is widely used in current and upcoming wireless standards. General-purpose graphics processor unit (GPGPU) is a programmable commodity processor that achieves high performance computation power by using many simple cores. In this paper, we present a 3GPP LTE compliant Turbo decoder accelerator that takes advantage of the processing power of GPU to offer fast Turbo decoding throughput. Several techniques are used to improve the performance of the decoder. To fully utilize the computational resources on GPU, our decoder can decode multiple codewords simultaneously, divide the workload for a single codeword across multiple cores, and pack multiple codewords to fit the single instruction multiple data (SIMD) instruction width. In addition, we use shared memory judiciously to enable hundreds of concurrent multiple threads while keeping frequently used data local to keep memory access fast. To improve efficiency of the decoder in the high SNR regime, we also present a low complexity early termination scheme based on average extrinsic LLR statistics. Finally, we examine how different workload partitioning choices affect the error correction performance and the decoder throughput.
Implementation of a High Throughput Soft MIMO Detector on GPU
(Springer, 2011-07-01) Wu, Michael; Sun, Yang; Gupta, Siddharth; Cavallaro, Joseph R.; Center for Multimedia Communication
Multiple-input multiple-output (MIMO) significantly increases the throughput of a communication system by employing multiple antennas at the transmitter and the receiver. To extract maximum performance from a MIMO system, a computationally intensive search based detector is needed. To meet the challenge of MIMO detection, typical suboptimal MIMO detectors are ASIC or FPGA designs. We aim to show that a MIMO detector on Graphic processor unit (GPU), a low-cost parallel programmable co-processor, can achieve high throughput and can serve as an alternative to ASIC/FPGA designs. However, careful architecture aware software design is needed to leverage the performance offered by GPU. We propose a novel soft MIMO detection algorithm, multi-pass trellis traversal (MTT), and show that we can achieve ASIC/FPGA-like performance and handle different configurations in software on GPU. The proposed design can be used to accelerate wireless physical layer simulations and to offload MIMO detection processing in wireless testbed platforms.
Implementation Trade-Offs For Linear Detection In Large-Scale MIMO Systems
(IEEE, 2013-06) Yin, Bei; Wu, Michael; Studer, Christoph; Cavallaro, Joseph R.; Dick, Chris
In this paper, we analyze the VLSI implementation tradeoffs for linear data detection in the uplink of large-scale multiple-input multiple-output (MIMO) wireless systems. Specifically, we analyze the error incurred by using the suboptimal, low-complexity matrix inverse proposed in Wu et al., 2013, ISCAS, and compare its performance and complexity to an exact matrix inversion algorithm. We propose a Cholesky-based reference architecture for exact matrix inversion and show corresponding implementation results on an Virtex-7 FPGA. Using this reference design, we perform a performance/complexity trade-off comparison with an FPGA implementation for the proposed approximate matrix inversion, which reveals that the inversion circuit of choice is determined by the antenna configuration (base-station antennas vs. number of users) of large-scale MIMO systems.
Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection
(Springer, 2017) Wu, Michael; Yin, Bei; Li, Kaipeng; Dick, Chris; Cavallaro, Joseph R.; Studer, Christoph
Massive multi-user (MU) MIMO wireless technology promises improved spectral efficiency compared to that of traditional cellular systems. While data-detection algorithms that rely on linear equalization achieve near-optimal error-rate performance for massive MU-MIMO systems, they require the solution to large linear systems at high throughput and low latency, which results in excessively high receiver complexity. In this paper, we investigate a variety of exact and approximate equalization schemes that solve the system of linear equations either explicitly (requiring the computation of a matrix inverse) or implicitly (by directly computing the solution vector). We analyze the associated performance/complexity trade-offs, and we show that for small base-station (BS)-to-user-antenna ratios, exact and implicit data detection using the Cholesky decomposition achieves near-optimal performance at low complexity. In contrast, implicit data detection using approximate equalization methods results in the best trade-off for large BS-to-user-antenna ratios. By combining the advantages of exact, approximate, implicit, and explicit matrix inversion, we develop a new frequency-adaptive e qualizer (FADE), which outperforms existing data-detection methods in terms of performance and complexity for wideband massive MU-MIMO systems.
Improving MIMO Sphere Detection Through Antenna Detection Order Scheduling
(Wireless Innovation Forum, 2011-12-01) Wu, Michael; Dick, Chris; Cavallaro, Joseph R.; Center for Multimedia Communication
This paper proposes a novel scalable Multiple-Input Multiple-Output (MIMO) detector that does not require preprocessing to achieve good bit error rate (BER) performance. MIMO processing is a key technology in broadband wireless technologies such as 3G LTE, WiMAX, and 802.11n. Existing detectors such as Flexsphere use preprocessing before MIMO detection to improve performance. Instead of costly preprocessing, the proposed detector schedules multiple search passes, where each search pass detects the transmit stream with a different permuted detection order. By changing the number of parallel search passes, we show that this scalable detector can achieve comparable performance to Flexsphere with reduced resource requirement, or can eliminate LLR clipping and achieve BER performance within 0.25 dB of exhaustive search with increased resource requirement.
Improving TLB Miss Handling with Page Table Pointer Caches
(1997-12-16) Wu, Michael; Zwaenepoel, Willy
Page table pointer caches are a hardware supplement for TLBs that cache pointers to pages of page table entries rather than page table entries themselves. A PTPC traps and handles most TLB misses in hardware with low overhead (usually a single memory access). PTPC misses are filled in software, allowing for an easy hardware implementation, similar in structure to a TLB. Since each PTPC entry refers to an entire page of page table entries, even a small PTPC maps a large amount of address space and achieves a very high hit rate. The primary goal of a PTPC is to lower TLB miss handling penalties. The combination of a TLB with a small PTPC provides good performance even in situations where standard TLBs alone perform badly (large workloads or multimedia applications). The advantage of this design is that we can continue to use small fixed size pages with standard TLBs. Since PTPCs use traditional page table structures and page sizes, they are very simple to implement in hardware and require minimal operating system modifications. Our simulations show that the addition of a PTPC to a system with a TLB can reduce miss handling costs by nearly an order of magnitude. Small PTPCs are extremely effective and the combination of small to medium sized TLBs coupled with smallPTPCs are an efficient alternative to large TLBs.
Low Complexity Opportunistic Decoder for Network Coding
(IEEE, 2012-12-01) Yin, Bei; Wu, Michael; Wang, Guohui; Cavallaro, Joseph R.; CMC
In this paper, we propose a novel opportunistic decoding scheme for network coding decoder which significantly reduces the decoder complexity and increases the throughput. Network coding was proposed to improve the network throughput and reliability, especially for multicast transmissions. Although network coding increases the network performance, the complexity of the network coding decoder algorithm is still high, especially for higher dimensional finite fields or larger network codes. Different software and hardware approaches were proposed to accelerate the decoding algorithm, but the decoder remains to be the bottleneck for high speed data transmission. We propose a novel decoding scheme which exploits the structure of the network coding matrix to reduce the network decoder complexity and improve throughput. We also implemented the proposed scheme on Virtex 7 FPGA and compared our implementation to the widely used Gaussian elimination.

Browsing by Author "Wu, Michael"

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