33 research outputs found
Deep Learning for Over-the-Air Non-Orthogonal Signal Classification
Non-cooperative communications, where a receiver can automatically
distinguish and classify transmitted signal formats prior to detection, are
desirable for low-cost and low-latency systems. This work focuses on the deep
learning enabled blind classification of multi-carrier signals covering their
orthogonal and non-orthogonal varieties. We define two signal groups, in which
Type-I includes signals with large feature diversity while Type-II has strong
feature similarity. We evaluate time-domain and frequency-domain convolutional
neural network (CNN) models in simulation with wireless channel/hardware
impairments. Simulation results reveal that the time-domain neural network
training is more efficient than its frequency-domain counterpart in terms of
classification accuracy and computational complexity. In addition, the
time-domain CNN models can classify Type-I signals with high accuracy but
reduced performance in Type-II signals because of their high signal feature
similarity. Experimental systems are designed and tested, using software
defined radio (SDR) devices, operated for different signal formats to form full
wireless communication links with line-of-sight and non-line-of-sight
scenarios. Testing, using four different time-domain CNN models, showed the
pre-trained CNN models to have limited efficiency and utility due to the
mismatch between the analytical/simulation and practical/real-world
environments. Transfer learning, which is an approach to fine-tune learnt
signal features, is applied based on measured over-the-air time-domain signal
samples. Experimental results indicate that transfer learning based CNN can
efficiently distinguish different signal formats in both line-of-sight and
non-line-of-sight scenarios with great accuracy improvement relative to the
non-transfer-learning approaches
Index Modulation Pattern Design for Non-Orthogonal Multicarrier Signal Waveforms
Spectral efficiency improvement is a key focus in most wireless communication
systems and achieved by various means such as using large antenna arrays and/or
advanced modulation schemes and signal formats. This work proposes to further
improve spectral efficiency through combining non-orthogonal spectrally
efficient frequency division multiplexing (SEFDM) systems with index modulation
(IM), which can efficiently make use of the indices of activated subcarriers as
communication information. Recent research has verified that IM may be used
with SEFDM to alleviate inter-carrier interference (ICI) and improve error
performance. This work proposes new SEFDM signal formats based on novel
activation pattern designs, which limit the locations of activated subcarriers
and enable a variable number of activated subcarriers in each SEFDM subblock.
SEFDM-IM system designs are developed by jointly considering activation
patterns, modulation schemes and signal waveform formats, with a set of
solutions evaluated under different spectral efficiency scenarios. Detailed
modelling of coded systems and simulation studies reveal that the proposed
designs not only lead to better bit error rate (BER) but also lower
peak-to-average power ratio (PAPR) and reduced computational complexity
relative to other reported index-modulated systems
Design and Prototyping of Hybrid Analogue Digital Multiuser MIMO Beamforming for Non-Orthogonal Signals
To enable user diversity and multiplexing gains, a fully digital precoding
multiple input multiple output (MIMO) architecture is typically applied.
However, a large number of radio frequency (RF) chains make the system
unrealistic to low-cost communications. Therefore, a practical three-stage
hybrid analogue-digital precoding architecture, occupying fewer RF chains, is
proposed aiming for a non-orthogonal IoT signal in low-cost multiuser MIMO
systems. The non-orthogonal waveform can flexibly save spectral resources for
massive devices connections or improve data rate without consuming extra
spectral resources. The hybrid precoding is divided into three stages including
analogue-domain, digital-domain and waveform-domain. A codebook based beam
selection simplifies the analogue-domain beamforming via phase-only tuning.
Digital-domain precoding can fine-tune the codebook shaped beam and resolve
multiuser interference in terms of both signal amplitude and phase. In the end,
the waveform-domain precoding manages the self-created inter carrier
interference (ICI) of the non-orthogonal signal. This work designs over-the-air
signal transmission experiments for fully digital and hybrid precoding systems
on software defined radio (SDR) devices. Results reveal that waveform precoding
accuracy can be enhanced by hybrid precoding. Compared to a transmitter with
the same RF chain resources, hybrid precoding significantly outperforms fully
digital precoding by up to 15.6 dB error vector magnitude (EVM) gain. A fully
digital system with the same number of antennas clearly requires more RF chains
and therefore is low power-, space- and cost- efficient. Therefore, the
proposed three-stage hybrid precoding is a quite suitable solution to
non-orthogonal IoT applications
Non-orthogonal signal transmission over nonlinear optical channels
The performance of spectrally efficient frequency division multiplexing (SEFDM) in optical communication systems is investigated considering the impact of fiber nonlinearities. Relative to orthogonal frequency division multiplexing (OFDM), sub-carriers within SEFDM signals are packed closer at a frequency spacing less than the symbol rate. In order to recover the data, a specially designed sphere decoding detector is used at the receiver end to compensate for the self-created inter carrier interference encountered in SEFDM signals. Our research demonstrated the benefits of the use of sphere decoding in SEFDM and also demonstrates the performance improvement of long-haul optical communication systems using SEFDM compared to the use of conventional OFDM, when fiber nonlinearities are considered. Different modulation formats ranging from4QAM to 32QAM are studied and it is shown that, for the same spectral efficiency and information rate, SEFDM signals allow a significant increase in the transmission distance compared to conventional OFDM signals
An Experimental Proof of Concept for Integrated Sensing and Communications Waveform Design
The integration of sensing and communication (ISAC) functionalities have
recently gained significant research interest as a hardware-, power-, spectrum-
and cost- efficient solution. This experimental work focuses on a
dual-functional radar sensing and communication framework where a single
radiation waveform, either omnidirectional or directional, can realize both
radar sensing and communication functions. We study a trade-off approach that
can balance the performance of communications and radar sensing. We design an
orthogonal frequency division multiplexing (OFDM) based multi-user multiple
input multiple output (MIMO) software-defined radio (SDR) testbed to validate
the dual-functional model. We carry out over-the-air experiments to investigate
the optimal trade-off factor to balance the performance for both functions. On
the radar performance, we measure the output beampatterns of our transmission
to examine their similarity to simulation based beampatterns. On the
communication side, we obtain bit error rate (BER) results from the testbed to
show the communication performance using the dual-functional waveform. Our
experiment reveals that the dual-functional approach can achieve comparable BER
performance with pure communication-based solutions while maintaining fine
radar beampatterns simultaneously
Intelligent non-cooperative optical networks : leveraging scattering neural networks with small training data
Artificial intelligence (AI) is enabling intelligent communications where learning based signal classification simplifies optical network signal allocation and shifts signal processing pressure to each network edge. This work proposes a non-orthogonal signal waveform framework that leverages its unique spectral compression characteristic as a user address for efficiently forwarding messages to target users. The primary focus of this work lies in the physical layer intelligent receiver design, which can automatically identify different received signal formats without preamble notification in a non-cooperative communication approach. Traditional signal classification methods, such as convolutional neural network (CNN), rely on extensive training, resulting in a heavy dependency on large training datasets. To overcome this limitation, this work designs a specific two-layer scattering neural network that can accurately separate signals even when the training data is limited, leading to reduced training complexity. Its performance remains robust in diverse transmission conditions. Furthermore, the scattering neural network is interpretable because features are extracted based on deterministic wavelet filters rather than training based filters
Deep intelligent spectral labelling and receiver signal distribution for optical links
A unique automatic receiver signal distribution strategy is proposed for private optical networks based on the concept of non-orthogonality. A non-orthogonal signal waveform can compress the spectral bandwidth, which not only fits a signal in a bandwidth limited scenario, but also enables the compression ratio information for labelling. Depending on a unique value of spectral compression, an end user destination can be correlated. A network edge node will rely on deep learning to intelligently identify each raw signal and forward it to corresponding end users with no sophisticated digital signal pre-processing. In this case, signal identification and distribution are faster while computationally intensive signal compensation and detection will be shifted to each end user since the receiver is highly dynamic and user-defined in private optical networks. An intelligent signal classifier will be trained considering various fiber transmission factors such as transmission distance, training dataset size and launch power. At the end, a universal classifier is obtained, which can be used to identify signals in a system for any fiber transmission distance and launch power
Practical Evaluations of SEFDM: Timing Offset and Multipath Impairments
The non-orthogonal signal waveform spectrally efficient frequency division multiplexing (SEFDM) improves spectral efficiency at the cost of self-created inter carrier interference (ICI). As the orthogonal property, similar to orthogonal frequency division multiplexing (OFDM), no longer exists, the robustness of SEFDM in realistic wireless environments might be weakened. This work aims to evaluate the sensitivity of SEFDM to practical channel distortions using a professional experiment testbed. First, timing offset is studied in a bypass channel to locate the imperfection of the testbed and its impact on SEFDM signals. Then, the joint effect of a multipath frequency selective channel and additive white Gaussian noise (AWGN) is investigated in the testbed. Through practical experiments, we demonstrate the performance of SEFDM in realistic radio frequency (RF) environments and verify two compensation methods for SEFDM. Our results show first frequency-domain compensation works well in frequency non-selective channel conditions while time-domain compensation method is suitable for frequency selective channel conditions. This work paves the way for the application of SEFDM in different channel scenarios