6,903 research outputs found
Multi-Antenna Assisted Virtual Full-Duplex Relaying with Reliability-Aware Iterative Decoding
In this paper, a multi-antenna assisted virtual full-duplex (FD) relaying
with reliability-aware iterative decoding at destination node is proposed to
improve system spectral efficiency and reliability. This scheme enables two
half-duplex relay nodes, mimicked as FD relaying, to alternatively serve as
transmitter and receiver to relay their decoded data signals regardless the
decoding errors, meanwhile, cancel the inter-relay interference with
QR-decomposition. Then, by deploying the reliability-aware iterative
detection/decoding process, destination node can efficiently mitigate
inter-frame interference and error propagation effect at the same time.
Simulation results show that, without extra cost of time delay and signalling
overhead, our proposed scheme outperforms the conventional selective
decode-and-forward (S-DF) relaying schemes, such as cyclic redundancy check
based S-DF relaying and threshold based S-DF relaying, by up to 8 dB in terms
of bit-error-rate.Comment: 6 pages, 4 figures, conference paper has been submitte
Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
This paper investigates the problem of resource allocation for a wireless
communication network with distributed reconfigurable intelligent surfaces
(RISs). In this network, multiple RISs are spatially distributed to serve
wireless users and the energy efficiency of the network is maximized by
dynamically controlling the on-off status of each RIS as well as optimizing the
reflection coefficients matrix of the RISs. This problem is posed as a joint
optimization problem of transmit beamforming and RIS control, whose goal is to
maximize the energy efficiency under minimum rate constraints of the users. To
solve this problem, two iterative algorithms are proposed for the single-user
case and multi-user case. For the single-user case, the phase optimization
problem is solved by using a successive convex approximation method, which
admits a closed-form solution at each step. Moreover, the optimal RIS on-off
status is obtained by using the dual method. For the multi-user case, a
low-complexity greedy searching method is proposed to solve the RIS on-off
optimization problem. Simulation results show that the proposed scheme achieves
up to 33\% and 68\% gains in terms of the energy efficiency in both single-user
and multi-user cases compared to the conventional RIS scheme and
amplify-and-forward relay scheme, respectively
Adaptive Modulation and Coding and Cooperative ARQ in a Cognitive Radio System
In this paper, a joint cross-layer design of adaptive modulation and coding
(AMC) and cooperative automatic repeat request (C-ARQ) scheme is proposed for a
secondary user in a shared-spectrum environment. First, based on the
statistical descriptions of the channel, closed-form expressions of the average
spectral efficiency (SE) and the average packet loss rate (PLR) are presented.
Then, the cross-layer scheme is designed, with the aim of maximizing the
average SE while maintaining the average PLR under a prescribed level. An
optimization problem is formed, and a sub-optimal solution is found: the target
packet error rates (PER) for the secondary system channels are obtained and the
corresponding sub-optimal AMC rate adaptation policy is derived based on the
target PERs. Finally, the average SE and the average PLR performance of the
proposed scheme are presented
Cloud-aided wireless systems: communications and radar applications
This dissertation focuses on cloud-assisted radio technologies for communication, including mobile cloud computing and Cloud Radio Access Network (C-RAN), and for radar systems.
This dissertation first concentrates on cloud-aided communications. Mobile cloud computing, which allows mobile users to run computationally heavy applications on battery limited devices, such as cell phones, is considered initially. Mobile cloud computing enables the offloading of computation-intensive applications from a mobile device to a cloud processor via a wireless interface. The interplay between offloading decisions at the application layer and physical-layer parameters, which determine the energy and latency associated with the mobile-cloud communication, motivates the inter-layer optimization of fine-grained task offloading across both layers. This problem is modeled by using application call graphs, and the joint optimization of application-layer and physical-layer parameters is carried out via a message passing algorithm by minimizing the total energy expenditure of the mobile user.
The concept of cloud radio is also being considered for the development of two cellular architectures known as Distributed RAN (D-RAN) and C-RAN, whereby the baseband processing of base stations is carried out in a remote Baseband Processing Unit (BBU). These architectures can reduce the capital and operating expenses of dense deployments at the cost of increasing the communication latency. The effect of this latency, which is due to the fronthaul transmission between the Remote Radio Head (RRH) and the BBU, is then studied for implementation of Hybrid Automatic Repeat Request (HARQ) protocols. Specifically, two novel solutions are proposed, which are based on the control-data separation architecture. The trade-offs involving resources such as the number of transmitting and receiving antennas, transmission power and the blocklength of the transmitted codeword, and the performance of the proposed solutions is investigated in analysis and numerical results.
The detection of a target in radar systems requires processing of the signal that is received by the sensors. Similar to cloud radio access networks in communications, this processing of the signals can be carried out in a remote Fusion Center (FC) that is connected to all sensors via limited-capacity fronthaul links. The last part of this dissertation is dedicated to exploring the application of cloud radio to radar systems. In particular, the problem of maximizing the detection performance at the FC jointly over the code vector used by the transmitting antenna and over the statistics of the noise introduced by quantization at the sensors for fronthaul transmission is investigated by adopting the information-theoretic criterion of the Bhattacharyya distance and information-theoretic bounds on the quantization rate
Maximizing CNN Accelerator Efficiency Through Resource Partitioning
Convolutional neural networks (CNNs) are revolutionizing machine learning,
but they present significant computational challenges. Recently, many
FPGA-based accelerators have been proposed to improve the performance and
efficiency of CNNs. Current approaches construct a single processor that
computes the CNN layers one at a time; the processor is optimized to maximize
the throughput at which the collection of layers is computed. However, this
approach leads to inefficient designs because the same processor structure is
used to compute CNN layers of radically varying dimensions.
We present a new CNN accelerator paradigm and an accompanying automated
design methodology that partitions the available FPGA resources into multiple
processors, each of which is tailored for a different subset of the CNN
convolutional layers. Using the same FPGA resources as a single large
processor, multiple smaller specialized processors increase computational
efficiency and lead to a higher overall throughput. Our design methodology
achieves 3.8x higher throughput than the state-of-the-art approach on
evaluating the popular AlexNet CNN on a Xilinx Virtex-7 FPGA. For the more
recent SqueezeNet and GoogLeNet, the speedups are 2.2x and 2.0x
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