skip to main content

Title: Neural Network-based Channel Estimation for 2x2 and 4x4 MIMO Communication in Noisy Channels
With increasing needs of fast and reliable commu- nication between devices, wireless communication techniques are rapidly evolving to meet such needs. Multiple input and output (MIMO) systems are one of the key techniques that utilize multiple antennas for high-throughput and reliable communication. However, increasing the number of antennas in communication also adds to the complexity of channel esti- mation, which is essential to accurately decode the transmitted data. Therefore, development of accurate and efficient channel estimation methods is necessary. We report the performance of machine learning-based channel estimation approaches to enhance channel estimation performance in high-noise envi- ronments. More specifically, bit error rate (BER) performance of 2 × 2 and 4 × 4 MIMO communication systems with space- time block coding model (STBC) and two neural network-based channel estimation algorithms is analyzed. Most significantly, the results demonstrate that a generalized regression neural network (GRNN) model matches BER results of a known-channel communication for 4 × 4 MIMO with 8-bit pilots, when trained in a specific signal to noise ratio (SNR) regime. Moreover, up to 9dB improvement in signal-to-noise ratio (SNR) for a target BER is observed, compared to least square (LS) channel estimation, especially when the model is trained in the more » low SNR regime. A deep artificial neural network (Deep ANN) model shows worse BER performance compared to LS in all tested environments. These preliminary results present an opportunity for achieving better performance in channel estimation through GRNN and highlight further research topics for deployment in the wild. « less
Authors:
; ; ; ;
Award ID(s):
1731833
Publication Date:
NSF-PAR ID:
10075910
Journal Name:
International Balkan Conference on Communications and Networking
Sponsoring Org:
National Science Foundation
More Like this
  1. Adaptive communication for Internet of Things (IoT) and Wireless Body Area Network (WBAN) technologies is becoming increasingly popular due to the large power-performance trade-offs and highly dynamic channel conditions. Path loss, low signal to noise ratio (SNR) in the channel and network congestion adversely affect the data communication, each of which can be taken care of using different strategies such as reducing the data rate (for reducing congestion), increasing the output power (for increased path loss) and application of error correction coding (ECC, for low SNR). In this paper, we present a digital-friendly Transceiver SoC consisting of an RF-DAC basedmore »transmitter with orthogonally tunable output power, data rate and ECC that enables optimum system level bit error rate (BER) and energy for over 3-orders of energy-performance scalability, along with an ultra-low-power OOK receiver that receives the transmitter's control bits from a nearby base station for closed-loop control. The data rate and ECC control is achieved through a digital baseband, while a tapped capacitor matching network controls the output power. The energy efficiency of the transmitter is 27.6pJ/b at 10MSps and at 0.8V supply (~9X improvement over state-of-the-art), while the entire SoC (Transmitter+OOK receiver for controller feedback) consumes only 41.5pJ/b.« less
  2. Overcoming the conventional trade-off between throughput and bit error rate (BER) performance, versus computational complexity is a long-term challenge for uplink Multiple-Input Multiple-Output (MIMO) detection in base station design for the cellular 5G New Radio roadmap, as well as in next generation wireless local area networks. In this work, we present ParaMax, a MIMO detector architecture that for the first time brings to bear physics-inspired parallel tempering algorithmic techniques [28, 50, 67] on this class of problems. ParaMax can achieve near optimal maximum-likelihood (ML) throughput performance in the Large MIMO regime, Massive MIMO systems where the base station has additionalmore »RF chains, to approach the number of base station antennas, in order to support even more parallel spatial streams. ParaMax is able to achieve a near ML-BER performance up to 160 × 160 and 80 × 80 Large MIMO for low-order modulations such as BPSK and QPSK, respectively, only requiring less than tens of processing elements. With respect to Massive MIMO systems, in 12 × 24 MIMO with 16-QAM at SNR 16 dB, ParaMax achieves 330 Mbits/s near-optimal system throughput with 4--8 processing elements per subcarrier, which is approximately 1.4× throughput than linear detector-based Massive MIMO systems.« less
  3. User demand for increasing amounts of wireless capacity continues to outpace supply, and so to meet this demand, significant progress has been made in new MIMO wireless physical layer techniques. Higher-performance systems now remain impractical largely only because their algorithms are extremely computationally demanding. For optimal performance, an amount of computation that increases at an exponential rate both with the number of users and with the data rate of each user is often required. The base station's computational capacity is thus becoming one of the key limiting factors on wireless capacity. QuAMax is the first large MIMO cloud-based radio accessmore »network design to address this issue by leveraging quantum annealing on the problem. We have implemented QuAMax on the 2,031 qubit D-Wave 2000Q quantum annealer, the state-of-the-art in the field. Our experimental results evaluate that implementation on real and synthetic MIMO channel traces, showing that 30 US of compute time on the 2000Q can enable 48 user, 48 AP antenna BPSK communication at 20 dB SNR with a bit error rate of 10^(-6) and a 1,500 byte frame error rate of 10^(-4).« less
  4. The present work reports on the novel implementation of a miniaturized receiver for underwater networking merging a Piezoelectric Micromachined Ultrasonic Transducer (PMUT) array and signal conditioning circuitry in a single, packaged device. Tests in both a large water tank and a pool demonstrated that the system can attain large enough Signal-to-Noise Ratio (SNR) for communication at distances beyond two meters. An actual communication test, implementing an Orthogonal Frequency Division Multiplexing (OFDM) scheme, was used to characterize the performance of the link in terms of Bit Error Rate (BER) vs SNR. In comparison to previous work demonstrating high-data rate communication formore »intra-body links and acoustic duplexing, this implementation allows for significantly larger distances of transmission, while addressing the signal conditioning and submersible packaging needs for underwater conditions, thus enabling PMUT arrays for operating as complete underwater communication receivers.« less
  5. Emerging wireless technologies employ MIMO beamforming antenna arrays to improve channel Signal-to-Noise Ratio (SNR). The increased dynamic range of channel SNR values that can be accommodated, creates power stress on Radio Frequency (RF) electronic circuitry. To alleviate this, we propose an approach in which the circuitry along with other transmission coding parameters can be dynamically tuned in response to channel SNR and beam-steering angle to either minimize power consumption or maximize throughput in the presence of manufacturing process variations while meeting a specified Bit Error Rate (BER) limit. The adaptation control policy is learned online and is facilitated by informationmore »obtained from testing of the RF circuitry before deployment.« less