Hardware experiments on multi-carrier waveforms for 5G

Weitkemper, Petra; Koppenborg, Johannes; Bazzi, Jamal; Rheinschmitt, R.; Kusume, Katsutoshi; Samardžija, Dragan; Fuchs, Rolf; Benjebbour, Anass

doi:10.1109/wcncw.2016.7552711

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The practical receiver with L p = 1024, L o = 512 attains a BER of 10 −5 at an SNR per bit of 5.5 dB, which is 1 dB better than the receiver with L p = 512, L o = 256. This is due to the fact that the variance of the channel estimation error with L p = 512 is twice that of L p = 1024 (see (31)). This difference in the variance of the channel estimation error affects the turbo decoding process.…”

Section: H Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Near Capacity Signaling over Fading Channels using Coherent Turbo Coded OFDM and Massive MIMO

Vasudevan

2017

Preprint

View full text Add to dashboard Cite

The minimum average signal-to-noise ratio (SNR) per bit required for error-free transmission over a fading channel is derived, and is shown to be equal to that of the additive white Gaussian noise (AWGN) channel, which is −1.6 dB. Discrete-time algorithms are presented for timing and carrier synchronization, as well as channel estimation, for turbo coded multiple input multiple output (MIMO) orthogonal frequency division multiplexed (OFDM) systems. Simulation results show that it is possible to achieve a bit error rate of 10 −5 at an average SNR per bit of 5.5 dB, using two transmit and two receive antennas. We then propose a near-capacity signaling method in which each transmit antenna uses a different carrier frequency. Using the near-capacity approach, we show that it is possible to achieve a BER of 2 × 10 −5 at an average SNR per bit of just 2.5 dB, with one receive antenna for each transmit antenna. When the number of receive antennas for each transmit antenna is increased to 128, then a BER of 2×10 −5 is attained at an average SNR per bit of 1.25 dB. In all cases, the number of transmit antennas is two and the spectral efficiency is 1 bit/transmission or 1 bit/sec/Hz. In other words, each transmit antenna sends 0.5 bit/transmission. It is possible to obtain higher spectral efficiency by increasing the number of transmit antennas, with no loss in BER performance, as long as each transmit antenna uses a different carrier frequency. The transmitted signal spectrum for the near-capacity approach can be restricted by pulse-shaping. In all the simulations, a four-state turbo code is used. The corresponding turbo decoder uses eight iterations. The algorithms can be implemented on programmable hardware and there is a large scope for parallel processing.

show abstract

Section: H Simulation Resultsmentioning

confidence: 99%

“…The values of L p and L d in Figure 1(a) have to be suitably increased to obtain a good estimate of the channel (see (31)) and maintain a high throughput (see 54). Let us denote the impulse response of the LPF by p n .…”

Section: Near Capacity Signalingmentioning

confidence: 99%

Near Capacity Signaling over Fading Channels using Coherent Turbo Coded OFDM and Massive MIMO

Vasudevan

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…In [23], low complexity pipelined architecture for FBMC/OQAM is proposed by optimizing in-built filter architecture resulting in 40-50% reduction in complexity. Please refer to [24] for more details of the various hardware implementation of waveform modulation techniques being considered in 5G.…”

Section: Related Workmentioning

confidence: 99%

Toward Intelligent Reconfigurable Wireless Physical Layer (PHY)

Singh

Santosh

Darak

2021

IEEE Open J. Circuits Syst.

View full text Add to dashboard Cite

Next-generation wireless networks are getting significant attention because they promise 10factor enhancement in mobile broadband along with the potential to enable new heterogeneous services. Services include massive machine type communications desired for Industrial 4.0 along with ultra-reliable low latency services for remote healthcare and vehicular communications. In this article, we present the design of intelligent and reconfigurable physical layer (PHY) to bring these services to reality. First, we design and implement the reconfigurable PHY via a hardware-software co-design approach on system-on-chip consisting of the ARM processor and field-programmable gate array (FPGA). The reconfigurable PHY is then made intelligent by augmenting it with online machine learning (OML) based decision-making algorithm. Such PHY can learn the environment (for example, wireless channel) and dynamically adapt the transceivers' configuration (i.e., modulation scheme, word-length) and select the wireless channel on-the-fly. Since the environment is unknown and changes with time, we make the OML architecture reconfigurable to enable dynamic switch between various OML algorithms on-the-fly. We have demonstrated the functional correctness of the proposed architecture for different environments and word-lengths. The detailed throughput, latency, and complexity analysis validate the feasibility and importance of the proposed intelligent and reconfigurable PHY in next-generation networks.INDEX TERMS Intelligent reconfigurable architecture, zynq SoC, physical layer, machine learning, multiarmed bandit.

show abstract

Measurements

Kishiyama¹,

Imai²

2019

5G Technology

View full text Add to dashboard Cite

Hardware experiments on multi-carrier waveforms for 5G

Cited by 3 publications

References 0 publications

Near Capacity Signaling over Fading Channels using Coherent Turbo Coded OFDM and Massive MIMO

Near Capacity Signaling over Fading Channels using Coherent Turbo Coded OFDM and Massive MIMO

Toward Intelligent Reconfigurable Wireless Physical Layer (PHY)

Measurements

Contact Info

Product

Resources

About