263 research outputs found
DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs
Low-resolution analog-to-digital converters (ADCs) have been considered as a
practical and promising solution for reducing cost and power consumption in
massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately,
low-resolution ADCs significantly distort the received signals, and thus make
data detection much more challenging. In this paper, we develop a new deep
neural network (DNN) framework for efficient and low-complexity data detection
in low-resolution massive MIMO systems. Based on reformulated maximum
likelihood detection problems, we propose two model-driven DNN-based detectors,
namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems,
respectively. The proposed OBMNet and FBMNet detectors have unique and simple
structures designed for low-resolution MIMO receivers and thus can be
efficiently trained and implemented. Numerical results also show that OBMNet
and FBMNet significantly outperform existing detection methods.Comment: 6 pages, 8 figures, submitted for publication. arXiv admin note: text
overlap with arXiv:2008.0375
Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs
The use of one-bit analog-to-digital converters (ADCs) is a practical
solution for reducing cost and power consumption in massive
Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused
by one-bit ADCs makes the data detection task much more challenging. In this
paper, we propose a two-stage detection method for massive MIMO systems with
one-bit ADCs. In the first stage, we propose several linear receivers based on
the Bussgang decomposition, that show significant performance gain over
existing linear receivers. Next, we reformulate the maximum-likelihood (ML)
detection problem to address its non-robustness. Based on the reformulated ML
detection problem, we propose a model-driven deep neural network-based
(DNN-based) receiver, whose performance is comparable with an existing support
vector machine-based receiver, albeit with a much lower computational
complexity. A nearest-neighbor search method is then proposed for the second
stage to refine the first stage solution. Unlike existing search methods that
typically perform the search over a large candidate set, the proposed search
method generates a limited number of most likely candidates and thus limits the
search complexity. Numerical results confirm the low complexity, efficiency,
and robustness of the proposed two-stage detection method.Comment: 12 pages, 10 figure
Miniaturized multisensor system with a thermal gradient: Performance beyond the calibration range
Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensing material (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as a nano-electronic nose to distinguish five different gases and estimate their concentrations. This innovative approach uses identical sensors working at different operating temperatures thanks to the thermal gradient created by an integrated microheater. A system with in-house developed hardware and software was used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitative discrimination of all gases after calibration. The system worked perfectly within the calibrated range (100% correct classification, 6.9% average error on concentration value). This work focuses on minimizing the number of points needed for calibration while maintaining good sensor performance, both for classification and error in estimating concentration. Therefore, the calibration range (in terms of gas concentration) was gradually reduced and further tests were performed with concentrations outside these new reduced limits. Although with only a few training points, down to just two per gas, the system performed well with 96% correct classifications and 31.7% average error for the gases at concentrations up to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower than the calibration range, the system worked less well, with 93% correct classifications and 38.6% average error, probably due to proximity to the limit of detection of the sensors
Multi-Agent Reinforcement Learning for Joint Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems
We consider the problem of joint channel assignment and power allocation in
underlaid cellular vehicular-to-everything (C-V2X) systems where multiple
vehicle-to-infrastructure (V2I) uplinks share the time-frequency resources with
multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and
autonomous vehicles to travel closely together. Due to the nature of fast
channel variant in vehicular environment, traditional centralized optimization
approach relying on global channel information might not be viable in C-V2X
systems with large number of users. Utilizing a reinforcement learning (RL)
approach, we propose a distributed resource allocation (RA) algorithm to
overcome this challenge. Specifically, we model the RA problem as a multi-agent
system. Based solely on the local channel information, each platoon leader, who
acts as an agent, collectively interacts with each other and accordingly
selects the optimal combination of sub-band and power level to transmit its
signals. Toward this end, we utilize the double deep Q-learning algorithm to
jointly train the agents under the objectives of simultaneously maximizing the
V2I sum-rate and satisfying the packet delivery probability of each V2V link in
a desired latency limitation. Simulation results show that our proposed
RL-based algorithm achieves a close performance compared to that of the
well-known exhaustive search algorithm.Comment: 6 pages, 4 figure
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