22,032 research outputs found
Turbo Packet Combining for Broadband Space-Time BICM Hybrid-ARQ Systems with Co-Channel Interference
In this paper, efficient turbo packet combining for single carrier (SC)
broadband multiple-input--multiple-output (MIMO) hybrid--automatic repeat
request (ARQ) transmission with unknown co-channel interference (CCI) is
studied. We propose a new frequency domain soft minimum mean square error
(MMSE)-based signal level combining technique where received signals and
channel frequency responses (CFR)s corresponding to all retransmissions are
used to decode the data packet. We provide a recursive implementation algorithm
for the introduced scheme, and show that both its computational complexity and
memory requirements are quite insensitive to the ARQ delay, i.e., maximum
number of ARQ rounds. Furthermore, we analyze the asymptotic performance, and
show that under a sum-rank condition on the CCI MIMO ARQ channel, the proposed
packet combining scheme is not interference-limited. Simulation results are
provided to demonstrate the gains offered by the proposed technique.Comment: 12 pages, 7 figures, and 2 table
Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation
The bit-error rate (BER) performance of a pulse position modulation (PPM) scheme for non-line-of-sight indoor optical links employing channel equalisation based on the artificial neural network (ANN) is reported. Channel equalisation is achieved by training a multilayer perceptrons ANN. A comparative study of the unequalised `soft' decision decoding and the `hard' decision decoding along with the neural equalised `soft' decision decoding is presented for different bit resolutions for optical channels with different delay spread. We show that the unequalised `hard' decision decoding performs the worst for all values of normalised delayed spread, becoming impractical beyond a normalised delayed spread of 0.6. However, `soft' decision decoding with/without equalisation displays relatively improved performance for all values of the delay spread. The study shows that for a highly diffuse channel, the signal-to-noise ratio requirement to achieve a BER of 10Γ’Λβ5 for the ANN-based equaliser is ~10 dB lower compared with the unequalised `soft' decoding for 16-PPM at a data rate of 155 Mbps. Our results indicate that for all range of delay spread, neural network equalisation is an effective tool of mitigating the inter-symbol interference
Delay Performance of MISO Wireless Communications
Ultra-reliable, low latency communications (URLLC) are currently attracting
significant attention due to the emergence of mission-critical applications and
device-centric communication. URLLC will entail a fundamental paradigm shift
from throughput-oriented system design towards holistic designs for guaranteed
and reliable end-to-end latency. A deep understanding of the delay performance
of wireless networks is essential for efficient URLLC systems. In this paper,
we investigate the network layer performance of multiple-input, single-output
(MISO) systems under statistical delay constraints. We provide closed-form
expressions for MISO diversity-oriented service process and derive
probabilistic delay bounds using tools from stochastic network calculus. In
particular, we analyze transmit beamforming with perfect and imperfect channel
knowledge and compare it with orthogonal space-time codes and antenna
selection. The effect of transmit power, number of antennas, and finite
blocklength channel coding on the delay distribution is also investigated. Our
higher layer performance results reveal key insights of MISO channels and
provide useful guidelines for the design of ultra-reliable communication
systems that can guarantee the stringent URLLC latency requirements.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Simultaneous Transmission and Reception: Algorithm, Design and System Level Performance
Full Duplex or Simultaneous transmission and reception (STR) in the same
frequency at the same time can potentially double the physical layer capacity.
However, high power transmit signal will appear at receive chain as echoes with
powers much higher than the desired received signal. Therefore, in order to
achieve the potential gain, it is imperative to cancel these echoes. As these
high power echoes can saturate low noise amplifier (LNA) and also digital
domain echo cancellation requires unrealistically high resolution
analog-to-digital converter (ADC), the echoes should be cancelled or suppressed
sufficiently before LNA. In this paper we present a closed-loop echo
cancellation technique which can be implemented purely in analogue domain. The
advantages of our method are multiple-fold: it is robust to phase noise, does
not require additional set of antennas, can be applied to wideband signals and
the performance is irrelevant to radio frequency (RF) impairments in transmit
chain. Next, we study a few protocols for STR systems in carrier sense multiple
access (CSMA) network and investigate MAC level throughput with realistic
assumptions in both single cell and multiple cells. We show that STR can reduce
hidden node problem in CSMA network and produce gains of up to 279% in maximum
throughput in such networks. Finally, we investigate the application of STR in
cellular systems and study two new unique interferences introduced to the
system due to STR, namely BS-BS interference and UE-UE interference. We show
that these two new interferences will hugely degrade system performance if not
treated appropriately. We propose novel methods to reduce both interferences
and investigate the performances in system level.Comment: 20 pages. This manuscript will appear in the IEEE Transactions on
Wireless Communication
Multiuser MIMO-OFDM for Next-Generation Wireless Systems
This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base stationβs or radio portβs coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems
Interference Alignment for Cognitive Radio Communications and Networks: A Survey
Β© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignmentβs theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe
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