4,605 research outputs found
Channel Estimation and Equalization for Cooperative Communication
The revolutionary concept of space-time coding introduced in the last decade has demonstrated that the deployment of multiple antennas at the transmitter allows for simultaneous increase in throughput and reliability because of the additional degrees of freedom offered by the spatial dimension of the wireless channel. However, the use of antenna arrays is not practical for deployment in some practical scenarios, e. g. , sensor networks, due to space and power limitations. A new form of realizing transmit diversity has been recently introduced under the name of user cooperation or cooperative diversity. The basic idea behind cooperative diversity rests on the observation that in a wireless environment, the signal transmitted by the source node is overheard by other nodes, which can be defined as "partners" or "relays". The source and its partners can jointly process and transmit their information, creating a "virtual antenna array" and therefore emulating transmit diversity. Most of the ongoing research efforts in cooperative diversity assume frequency flat channels with perfect channel knowledge. However, in practical scenarios, e. g. broadband wireless networks, these assumptions do not apply. Frequency-selective fading and imperfect channel knowledge should be considered as a more realistic channel model. The development of equalization and channel estimation algorithms play a crucial element in the design of digital receivers as their accuracy determine the overall performance. This dissertation creates a framework for designing and analyzing various time and frequency domain equalization schemes, i. e. distributed time reversal (D-TR) STBC, distributed single carrier frequency domain (D-SC-FDE) STBC, and distributed orthogonal frequency division multiplexing (D-OFDM) STBC schemes, for broadband cooperative communication systems. Exploiting the orthogonally embedded in D-STBCs, we were able to maintain low-decoding complexity for all underlying schemes, thus, making them excellent candidates for practical scenarios, such as multi-media broadband communication systems. Furthermore, we propose and analyze various non-coherent and channel estimation algorithms to improve the quality and reliability of wireless communication networks. Specifically, we derive a non-coherent decoding rule which can be implemented in practice by a Viterbi-type algorithm. We demonstrate through the derivation of a pairwise error probability expression that the proposed non-coherent detector guarantees full diversity. Although this decoding rule has been derived assuming quasi-static channels, its inherent channel tracking capability allows its deployment over time-varying channels with a promising performance as a sub-optimal solution. As a possible alternative to non-coherent detection, we also investigate the performance of mismatched-coherent receiver, i. e. , coherent detection with imperfect channel estimation. Our performance analysis demonstrates that the mismatched-coherent receiver is able to collect the full diversity as its non-coherent competitor over quasi-static channels. Finally, we investigate and analyze the effect of multiple antennas deployment at the cooperating terminals assuming different relaying techniques. We derive pairwise error probability expressions quantifying analytically the impact of multiple antenna deployment at the source, relay and/or destination terminals on the diversity order for each of the relaying methods under consideration
Semantic Communication for Cooperative Perception based on Importance Map
Cooperative perception, which has a broader perception field than
single-vehicle perception, has played an increasingly important role in
autonomous driving to conduct 3D object detection. Through vehicle-to-vehicle
(V2V) communication technology, various connected automated vehicles (CAVs) can
share their sensory information (LiDAR point clouds) for cooperative
perception. We employ an importance map to extract significant semantic
information and propose a novel cooperative perception semantic communication
scheme with intermediate fusion. Meanwhile, our proposed architecture can be
extended to the challenging time-varying multipath fading channel. To alleviate
the distortion caused by the time-varying multipath fading, we adopt explicit
orthogonal frequency-division multiplexing (OFDM) blocks combined with channel
estimation and channel equalization. Simulation results demonstrate that our
proposed model outperforms the traditional separate source-channel coding over
various channel models. Moreover, a robustness study indicates that only part
of semantic information is key to cooperative perception. Although our proposed
model has only been trained over one specific channel, it has the ability to
learn robust coded representations of semantic information that remain
resilient to various channel models, demonstrating its generality and
robustness.Comment: 13 pages,22 figures;journal;submitted for possible publicatio
Pilot Decontamination in CMT-based Massive MIMO Networks
Pilot contamination problem in massive MIMO networks operating in
time-division duplex (TDD) mode can limit their expected capacity to a great
extent. This paper addresses this problem in cosine modulated multitone (CMT)
based massive MIMO networks; taking advantage of their so-called blind
equalization property. We extend and apply the blind equalization technique
from single antenna case to multi-cellular massive MIMO systems and show that
it can remove the channel estimation errors (due to pilot contamination effect)
without any need for cooperation between different cells or transmission of
additional training information. Our numerical results advocate the efficacy of
the proposed blind technique in improving the channel estimation accuracy and
removal of the residual channel estimation errors caused by the users of the
other cells.Comment: Accepted in ISWCS 201
Diffusive MIMO Molecular Communications: Channel Estimation, Equalization and Detection
In diffusion-based communication, as for molecular systems, the achievable
data rate is low due to the stochastic nature of diffusion which exhibits a
severe inter-symbol-interference (ISI). Multiple-Input Multiple-Output (MIMO)
multiplexing improves the data rate at the expense of an inter-link
interference (ILI). This paper investigates training-based channel estimation
schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization
methods. Maximum likelihood and least-squares estimators of mean channel are
derived, and the training sequence is designed to minimize the mean square
error (MSE). Numerical validations in terms of MSE are compared with Cramer-Rao
bound derived herein. Equalization is based on decision feedback equalizer
(DFE) structure as this is effective in mitigating diffusive ISI/ILI.
Zero-forcing, minimum MSE and least-squares criteria have been paired to DFE,
and their performances are evaluated in terms of bit error probability. Since
D-MIMO systems are severely affected by the ILI because of short transmitters
inter-distance, D-MIMO time interleaving is exploited as countermeasure to
mitigate the ILI with remarkable performance improvements. The feasibility of a
block-type communication including training and data equalization is explored
for D-MIMO, and system-level performances are numerically derived.Comment: Accepted paper at IEEE transaction on Communicatio
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