12 research outputs found
Code designs for MIMO broadcast channels
Recent information-theoretic results show the optimality of dirty-paper coding (DPC) in achieving the full capacity region of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). This paper presents a DPC based code design for BCs. We consider the case in which there is an individual rate/signal-to-interference-plus-noise ratio (SINR) constraint for each user. For a fixed transmitter power, we choose the linear transmit precoding matrix such that the SINRs at users are uniformly maximized, thus ensuring the best bit-error rate performance. We start with Cover's simplest two-user Gaussian BC and present a coding scheme that operates 1.44 dB from the boundary of the capacity region at the rate of one bit per real sample (b/s) for each user. We then extend the coding strategy to a two-user MIMO Gaussian BC with two transmit antennas at the base-station and develop the first limit-approaching code design using nested turbo codes for DPC. At the rate of 1 b/s for each user, our design operates 1.48 dB from the capacity region boundary. We also consider the performance of our scheme over a slow fading BC. For two transmit antennas, simulation results indicate a performance loss of only 1.4 dB, 1.64 dB and 1.99 dB from the theoretical limit in terms of the total transmission power for the two, three and four user case, respectively
Decision-Feedback Detection for Bidirectional Molecular Relaying with Direct Links
In this paper, we consider bidirectional relaying between two diffusion-based
molecular transceivers (bio-nodes). As opposed to existing literature, we
incorporate the effect of direct diffusion links between the nodes and leverage
it to improve performance. Assuming network coding type operation at the relay,
we devise a detection strategy, based on the maximum-likelihood principle, that
combines the signal received from the relay and that received from the direct
link. At the same time, since a diffusion-based molecular communication channel
is characterized by high inter-symbol interference (ISI), we utilize a decision
feedback mechanism to mitigate its effect. Simulation results indicate that the
proposed setup incorporating the direct link can achieve notable improvement in
error performance over conventional detection schemes that do not exploit the
direct link and/or do not attempt to mitigate the effect of ISI
Code design for multiple-input multiple-output broadcast channels
Recent information theoretical results indicate that dirty-paper coding (DPC)
achieves the entire capacity region of the Gaussian multiple-input multiple-output
(MIMO) broadcast channel (BC). This thesis presents practical code designs for
Gaussian BCs based on DPC. To simplify our designs, we assume constraints on
the individual rates for each user instead of the customary constraint on transmitter
power. The objective therefore is to minimize the transmitter power such that
the practical decoders of all users are able to operate at the given rate constraints.
The enabling element of our code designs is a practical DPC scheme based on nested
turbo codes. We start with Cover's simplest two-user Gaussian BC as a toy example
and present a code design that operates 1.44 dB away from the capacity region
boundary at the transmission rate of 1 bit per sample per dimension for each user.
Then we consider the case of the multiple-input multiple-output BC and develop a
practical limit-approaching code design under the assumption that the channel state
information is available perfectly at the receivers as well as at the transmitter. The
optimal precoding strategy in this case can be derived by invoking duality between
the MIMO BC and MIMO multiple access channel (MAC). However, this approach
requires transformation of the optimal MAC covariances to their corresponding counterparts
in the BC domain. To avoid these computationally complex transformations,
we derive a closed-form expression for the optimal precoding matrix for the two-user
case and use it to determine the optimal precoding strategy. For more than two users we propose a low-complexity suboptimal strategy, which, for three transmit antennas
at the base station and three users (each with a single receive antenna), performs
only 0.2 dB worse than the optimal scheme.
Our obtained results are only 1.5 dB away from the capacity limit. Moreover
simulations indicate that our practical DPC based scheme significantly outperforms
the prevalent suboptimal strategies such as time division multiplexing and zero forcing
beamforming. The drawback of DPC based designs is the requirement of channel state
information at the transmitter. However, if the channel state information can be
communicated back to the transmitter effectively, DPC does indeed have a promising
future in code designs for MIMO BCs
Coding for Cooperative Communications
The area of cooperative communications has received tremendous research interest
in recent years. This interest is not unwarranted, since cooperative communications
promises the ever-so-sought after diversity and multiplexing gains typically
associated with multiple-input multiple-output (MIMO) communications, without
actually employing multiple antennas. In this dissertation, we consider several cooperative
communication channels, and for each one of them, we develop information
theoretic coding schemes and derive their corresponding performance limits. We next
develop and design practical coding strategies which perform very close to the information
theoretic limits.
The cooperative communication channels we consider are: (a) The Gaussian relay
channel, (b) the quasi-static fading relay channel, (c) cooperative multiple-access
channel (MAC), and (d) the cognitive radio channel (CRC). For the Gaussian relay
channel, we propose a compress-forward (CF) coding strategy based on Wyner-Ziv
coding, and derive the achievable rates specifically with BPSK modulation. The CF
strategy is implemented with low-density parity-check (LDPC) and irregular repeataccumulate
codes and is found to operate within 0.34 dB of the theoretical limit. For
the quasi-static fading relay channel, we assume that no channel state information
(CSI) is available at the transmitters and propose a rateless coded protocol which
uses rateless coded versions of the CF and the decode-forward (DF) strategy. We
implement the protocol with carefully designed Raptor codes and show that the implementation suffers a loss of less than 10 percent from the information theoretical limit. For
the MAC, we assume quasi-static fading, and consider cooperation in the low-power
regime with the assumption that no CSI is available at the transmitters. We develop
cooperation methods based on multiplexed coding in conjunction with rateless
codes and find the achievable rates and in particular the minimum energy per bit to
achieve a certain outage probability. We then develop practical coding methods using
Raptor codes, which performs within 1.1 dB of the performance limit. Finally, we
consider a CRC and develop a practical multi-level dirty-paper coding strategy using
LDPC codes for channel coding and trellis-coded quantization for source coding. The
designed scheme is found to operate within 0.78 dB of the theoretical limit.
By developing practical coding strategies for several cooperative communication
channels which exhibit performance close to the information theoretic limits, we show
that cooperative communications not only provide great benefits in theory, but can
possibly promise the same benefits when put into practice. Thus, our work can be
considered a useful and necessary step towards the commercial realization of cooperative
communications
A Deep-Unfolded Spatiotemporal RPCA Network For L+S Decomposition
Low-rank and sparse decomposition based methods find their use in many
applications involving background modeling such as clutter suppression and
object tracking. While Robust Principal Component Analysis (RPCA) has achieved
great success in performing this task, it can take hundreds of iterations to
converge and its performance decreases in the presence of different phenomena
such as occlusion, jitter and fast motion. The recently proposed deep unfolded
networks, on the other hand, have demonstrated better accuracy and improved
convergence over both their iterative equivalents as well as over other neural
network architectures. In this work, we propose a novel deep unfolded
spatiotemporal RPCA (DUST-RPCA) network, which explicitly takes advantage of
the spatial and temporal continuity in the low-rank component. Our experimental
results on the moving MNIST dataset indicate that DUST-RPCA gives better
accuracy when compared with the existing state of the art deep unfolded RPCA
networks
Feature Selection on Sentinel-2 Multi-spectral Imagery for Efficient Tree Cover Estimation
This paper proposes a multi-spectral random forest classifier with suitable
feature selection and masking for tree cover estimation in urban areas. The key
feature of the proposed classifier is filtering out the built-up region using
spectral indices followed by random forest classification on the remaining mask
with carefully selected features. Using Sentinel-2 satellite imagery, we
evaluate the performance of the proposed technique on a specified area
(approximately 82 acres) of Lahore University of Management Sciences (LUMS) and
demonstrate that our method outperforms a conventional random forest classifier
as well as state-of-the-art methods such as European Space Agency (ESA)
WorldCover 10m 2020 product as well as a DeepLabv3 deep learning architecture.Comment: IEEE IGARSS 202
Nested turbo codes for the costa problem
Driven by applications in data-hiding, MIMO broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source- channel coding for algebraic binning. We then address practical code design based on nested lattice codes and propose nested turbo codes using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to TCQ, turbo-like TCQ offers structural similarity between the source and channel coding components, leading to more efficient nesting with TTCM and better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. Optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions and exhibits a gap of 0.94, 1.42 and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 bits/sample, respectively
Logistics Hub Location Optimization: A K-Means and P-Median Model Hybrid Approach Using Road Network Distances
Logistic hubs play a pivotal role in the last-mile delivery distance; even a
slight increment in distance negatively impacts the business of the e-commerce
industry while also increasing its carbon footprint. The growth of this
industry, particularly after Covid-19, has further intensified the need for
optimized allocation of resources in an urban environment. In this study, we
use a hybrid approach to optimize the placement of logistic hubs. The approach
sequentially employs different techniques. Initially, delivery points are
clustered using K-Means in relation to their spatial locations. The clustering
method utilizes road network distances as opposed to Euclidean distances.
Non-road network-based approaches have been avoided since they lead to
erroneous and misleading results. Finally, hubs are located using the P-Median
method. The P-Median method also incorporates the number of deliveries and
population as weights. Real-world delivery data from Muller and Phipps (M&P) is
used to demonstrate the effectiveness of the approach. Serving deliveries from
the optimal hub locations results in the saving of 815 (10%) meters per
delivery
Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods
A reliable yet inexpensive tool for the estimation of flood water spread is
conducive for efficient disaster management. The application of optical and SAR
imagery in tandem provides a means of extended availability and enhanced
reliability of flood mapping. We propose a methodology to merge these two types
of imagery into a common data space and demonstrate its use in the
identification of affected populations and infrastructure for the 2022 floods
in Pakistan. The merging of optical and SAR data provides us with improved
observations in cloud-prone regions; that is then used to gain additional
insights into flood mapping applications. The use of open source datasets from
WorldPop and OSM for population and roads respectively makes the exercise
globally replicable. The integration of flood maps with spatial data on
population and infrastructure facilitates informed policy design. We have shown
that within the top five flood-affected districts in Sindh province, Pakistan,
the affected population accounts for 31 %, while the length of affected roads
measures 1410.25 km out of a total of 7537.96 km.Comment: IEEE IGARSS 202