4,329 research outputs found
On Optimal TCM Encoders
An asymptotically optimal trellis-coded modulation (TCM) encoder requires the
joint design of the encoder and the binary labeling of the constellation. Since
analytical approaches are unknown, the only available solution is to perform an
exhaustive search over the encoder and the labeling. For large constellation
sizes and/or many encoder states, however, an exhaustive search is unfeasible.
Traditional TCM designs overcome this problem by using a labeling that follows
the set-partitioning principle and by performing an exhaustive search over the
encoders. In this paper we study binary labelings for TCM and show how they can
be grouped into classes, which considerably reduces the search space in a joint
design. For 8-ary constellations, the number of different binary labelings that
must be tested is reduced from 8!=40320 to 240. For the particular case of an
8-ary pulse amplitude modulation constellation, this number is further reduced
to 120 and for 8-ary phase shift keying to only 30. An algorithm to generate
one labeling in each class is also introduced. Asymptotically optimal TCM
encoders are tabulated which are up to 0.3 dB better than the previously best
known encoders
Low Complexity Blind Equalization for OFDM Systems with General Constellations
This paper proposes a low-complexity algorithm for blind equalization of data
in OFDM-based wireless systems with general constellations. The proposed
algorithm is able to recover data even when the channel changes on a
symbol-by-symbol basis, making it suitable for fast fading channels. The
proposed algorithm does not require any statistical information of the channel
and thus does not suffer from latency normally associated with blind methods.
We also demonstrate how to reduce the complexity of the algorithm, which
becomes especially low at high SNR. Specifically, we show that in the high SNR
regime, the number of operations is of the order O(LN), where L is the cyclic
prefix length and N is the total number of subcarriers. Simulation results
confirm the favorable performance of our algorithm
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future
wireless communications owing to its potentials for cost-effective network
deployment and high data rate. There are several implementation issues in the
OWC which have not been encountered in radio frequency wireless communications.
First, practical OWC transmitters need an illumination control on color,
intensity, and luminance, etc., which poses complicated modulation design
challenges. Furthermore, signal-dependent properties of optical channels raise
non-trivial challenges both in modulation and demodulation of the optical
signals. To tackle such difficulties, deep learning (DL) technologies can be
applied for optical wireless transceiver design. This article addresses recent
efforts on DL-based OWC system designs. A DL framework for emerging image
sensor communication is proposed and its feasibility is verified by simulation.
Finally, technical challenges and implementation issues for the DL-based
optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on
Applications of Artificial Intelligence in Wireless Communication
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