Spectrum-Efficient Triple-Layer Hybrid Optical OFDM for IM/DD-Based Optical Wireless Communications

In this paper, a triple-layer hybrid optical orthogonal frequency division multiplexing (THO-OFDM) for intensity modulation with direct detection (IM/DD) systems with a high spectral efficiency is proposed. We combine N-point asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM), N/2-point ACO-OFDM, and N/2-point pulse amplitude modulated discrete multitoned (PAM-DMT) in a single frame for simultaneous transmission. The time- and frequency-domain demodulation methods are introduced by fully exploiting the special structure of the proposed THO-OFDM. Theoretical analysis show that, the proposed THO-OFDM can reach the spectral efficiency limit of the conventional layered ACO-OFDM (LACO-OFDM). Simulation results demonstrate that, the time-domain receiver offers improved bit error rate (BER) performance compared with the frequency-domain with ∼40% reduced computation complexity when using 512 subcarriers. Furthermore, we show a 3 dB improvement in the peak-to-average power ratio (PAPR) compared with LACO-OFDM for the same three layers

On Low Complexity Detection for QAM Isomorphic Constellations

Despite of the known gap from the Shannon's capacity, several standards are still employing QAM or star shape constellations, mainly due to the existing low complexity detectors. In this paper, we investigate the low complexity detection for a family of QAM isomorphic constellations. These constellations are known to perform very close to the peak-power limited capacity, outperforming the DVB-S2X standard constellations. The proposed strategy is to first remap the received signals to the QAM constellation using the existing isomorphism and then break the log likelihood ratio computations to two one dimensional PAM constellations. Gains larger than 0.6 dB with respect to QAM can be obtained over the peak power limited channels without any increase in detection complexity. Our scheme also provides a systematic way to design constellations with low complexity one dimensional detectors. Several open problems are discussed at the end of the paper.Comment: Submitted to IEEE GLOBECOM 201

A (Simplified) Bluetooth Maximum a Posteriori Probability (Map) Receiver

In our software-defined radio project, we aim at combining two standards luetooth and HIPERLAN/2. The HIPERLAN/2 receiver requires more computational power than Bluetooth. We choose to use this computational power also for Bluetooth and look for more advanced demodulation algorithms such as a maximum a posteriori probability (MAP) receiver. The paper discusses a simplified MAP receiver for Bluetooth GFSK signals. Laurent decomposition provides an orthogonal vector space for the MAP receiver. As the first Laurent waveform contains the most energy, we have used only this waveform for our (simplified) MAP receiver. This receiver requires a E/sub b//N/sub 0/ of about 11 dB for a BER of 10/sup -3/, required by the Bluetooth standard. This value is about 6 dB better than single bit demodulators. This performance is only met if the receiver has exact knowledge of the modulation index

Learning How to Demodulate from Few Pilots via Meta-Learning

Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training in order to learn a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data