ASIC implementation and performance comparison of adaptive detection for MIMO-OFDM system

Suikkanen, Essi; Juntti, Markku

doi:10.1109/acssc.2015.7421424

“…The simulation results presented in [9][10][11][12] show that in an uncorrelated channel, the LMMSE detector is able to outperform the K-best LSD, but as the correlation increases, the opportunities to use the LMMSE detector become scarce. In the 8×8 MIMO system, the LMMSE detector is unable to separate all the spatial streams, and, therefore, it is performing poorly compared to the K-best LSD.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…The performances of the LMMSE and K-best detectors in 4 × 4 and 8 × 8 MIMO-OFDM systems with HARQ were compared in [11]. The performance-energy efficiency comparison between the K-best LSD and LMMSE detector were presented in [12] for the 4 × 4 and 8 × 8 systems with HARQ.…”

Section: Introductionmentioning

confidence: 99%

Performance and ASIC Designs of the K-best LSD and LMMSE Detectors for LTE Downlink

Suikkanen

¹

,

Juntti

²

2019

J Sign Process Syst

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We consider performance comparison and application specific integrated circuit (ASIC) designs of linear minimum mean-square error (LMMSE) and Kbest list sphere detector (LSD) algorithms for 4 × 4 and 8 × 8 multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Requirements for higher data rate and lower power consumption set new challenges for implementation. In order to minimize the power consumption, an optimal detector would be able to switch the detection algorithm to suit the channel conditions. The detectors are designed for three different modulation schemes using 28 nm complementary metal oxide semiconductor (CMOS) technology. The communications performance is evaluated in the Third Generation Partnership Project (3GPP) Long-Term Evolution (LTE) system. The impact of transmit precoding is considered. The ASIC designs aim at providing the hardware design aspects to the comparison of detectors. The designs are synthesized and complexity and power consumption results are found. Based on the ASIC synthesis and communications performance results, we show the performance-energy efficiency and performance-complexity comparison. We also present the most suitable scenarios for a low-power This research was financially supported in part by Tekes, the

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“…Recent implementations include [90,117,118,17,155,157,156,162]. The recent work by Suikkanen [157,156] illustrates the trade-off between the receiver energy efficiency and useful data rate or goodput, which is defined as the minimum of the detection rate enabled by the receiver hardware and useful throughput of the communications system [90]. The latter depends on the error rate performance and the nominal data rate such that the value gives the error free or reliable transmission rate, practically achieved via hybrid automatic repeat request (HARQ) protocol with price of introduced latency.…”

Section: Implementationsmentioning

confidence: 99%

Signal Processing for Wireless Transceivers

Juntti

¹

,

Renfors

²

,

Valkama

³

2013

Handbook of Signal Processing Systems

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The data rates as well as quality of service (QoS) requirements for rich user experience in wireless communication services are continuously growing. While consuming a major portion of the energy needed by wireless devices, the wireless transceivers have a key role in guaranteeing the needed data rates with high bandwidth efficiency. The cost of wireless devices also heavily depends on the transmitter and receiver technologies. In this chapter, we concentrate on the problem of transmitting information sequences efficiently through a wireless channel and performing reception such that it can be implemented with state of the art signal processing tools. The operations of the wireless devices can be divided to RF and baseband (BB) processing. Our emphasis is to cover the BB part, including the coding, modulation, and waveform generation functions, which are mostly using the tools and techniques from digital signal processing. But we also look at the overall transceiver from the RF system point of view, covering issues like frequency translations and channelization filtering, as well as emerging techniques for mitigating the inevitable imperfections of the analog RF circuitry through advanced digital signal processing methods.

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Signal Processing for Wireless Transceivers

Renfors

¹

,

Juntti

²

,

Valkama

³

2018

Handbook of Signal Processing Systems

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The data rates as well as quality of service (QoS) requirements for rich user experience in wireless communication services are continuously growing. While consuming a major portion of the energy needed by wireless devices, the wireless transceivers have a key role in guaranteeing the needed data rates with high bandwidth efficiency. The cost of wireless devices also heavily depends on the transmitter and receiver technologies. In this chapter, we concentrate on the problem of transmitting information sequences efficiently through a wireless channel and performing reception such that it can be implemented with state of the art signal processing tools. The operations of the wireless devices can be divided to RF and baseband (BB) processing. Our emphasis is to cover the BB part, including the coding, modulation, and waveform generation functions, which are mostly using the tools and techniques from digital signal processing. But we also look at the overall transceiver from the RF system point of view, covering issues like frequency translations and channelization filtering, as well as emerging techniques for mitigating the inevitable imperfections of the analog RF circuitry through advanced digital signal processing methods.

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ASIC implementation and performance comparison of adaptive detection for MIMO-OFDM system

Cited by 3 publications

References 15 publications

Performance and ASIC Designs of the K-best LSD and LMMSE Detectors for LTE Downlink

Performance and ASIC Designs of the K-best LSD and LMMSE Detectors for LTE Downlink

Signal Processing for Wireless Transceivers

Signal Processing for Wireless Transceivers

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