110 research outputs found
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
Accelerating BLAST Computation on an FPGA-enhanced PC Cluster
This paper introduces an FPGA-based scheme to accelerate mpiBLAST, which is a parallel sequence alignment algorithm for computational biology. Recent rapidly growing biological databases for sequence alignment require highthroughput storage and network rather than computing speed. Our scheme utilizes a specialized hardware configured on an FPGA-board which connects flash storage and other FPGAboards directly. The specialized hardware configured on the FPGAs, we call a Data Stream Processing Engine (DSPE), take a role for preprocessing to adjust data for high-performance multi- and many- core processors simultaneously with offloading system-calls for storage access and networking. DSPE along the datapath achieves in-datapath computing which applies operations for data streams passing through the FPGA. Two functions in mpiBLAST are implemented using DSPE to offload operations along the datapath. The first function is database partitioning, which distributes the biological database to multiple computing nodes before commencing the BLAST processes. Using DSPE, we observe a 20-fold improvement in computation time for the database partitioning operation. The second function is an early part of the BLAST process that determines the positions of sequences for more detailed computations. We implement IDP-BLAST (In-datapath BLAST), which annotates positions in data streams from solid-state drives. We show that IDP-BLAST accelerates the computation time of the preprocess of BLAST by a factor of three hundred by offloading heavy operations to the introduced special hardware
A Light-weight Content Distribution Scheme for Cooperative Caching in Telco-CDNs
A key technique to reduce the rapid growing of video-on-demand’s traffic is a cooperative caching strategy aggregating multiple cache storages. Many internet service providers have considered the use of cache servers on their networks as a solution to reduce the traffic. Existing schemes often periodically calculate a sub-optimal allocation of the content caches in the network. However, such approaches require a large computational overhead that cannot be amortized in a presence of frequent changes of the contents’ popularities. This paper proposes a light-weight scheme for a cooperative caching that obtains a sub-optimal distribution of the contents by focusing on their popularities. This was made possible by adding color tags to both cache servers and contents. In addition, we propose a hybrid caching strategy based on Least Frequently Used (LFU) and Least Recently Used (LRU) schemes, which efficiently manages the contents even with a frequent change in the popularity. Evaluation results showed that our light-weight scheme could considerably reduce the traffic, reaching a sub-optimal result. In addition, the performance gain is obtained with a computation overhead of just a few seconds. The evaluation results also showed that the hybrid caching strategy could follow the rapid variation of the popularity. While a single LFU strategy drops the hit ratio by 13.9%, affected by rapid popularity changes, our proposed hybrid strategy could limit the degradation to only 2.3%
Performance-Oriented Design for Intelligent Reflecting Surface Assisted Federated Learning
To efficiently exploit the massive amounts of raw data that are increasingly
being generated in mobile edge networks, federated learning (FL) has emerged as
a promising distributed learning technique. By collaboratively training a
shared learning model on edge devices, raw data transmission and storage are
replaced by the exchange of the local computed parameters/gradients in FL,
which thus helps address latency and privacy issues. However, the number of
resource blocks when using traditional orthogonal transmission strategies for
FL linearly scales with the number of participating devices, which conflicts
with the scarcity of communication resources. To tackle this issue,
over-the-air computation (AirComp) has emerged recently which leverages the
inherent superposition property of wireless channels to perform one-shot model
aggregation. However, the aggregation accuracy in AirComp suffers from the
unfavorable wireless propagation environment. In this paper, we consider the
use of intelligent reflecting surfaces (IRSs) to mitigate this problem and
improve FL performance with AirComp. Specifically, a performance-oriented
design scheme that directly minimizes the optimality gap of the loss function
is proposed to accelerate the convergence of AirComp-based FL. We first analyze
the convergence behavior of the FL procedure with the absence of channel fading
and noise. Based on the obtained optimality gap which characterizes the impact
of channel fading and noise in different communication rounds on the ultimate
performance of FL, we propose both online and offline approaches to tackle the
resulting design problem. Simulation results demonstrate that such a
performance-oriented design strategy can achieve higher test accuracy than the
conventional isolated mean square error (MSE) minimization approach in FL.Comment: This work has been submitted to the IEEE for possible publicatio
Computation Offloading for Edge Computing in RIS-Assisted Symbiotic Radio Systems
In the paper, we investigate the coordination process of sensing and
computation offloading in a reconfigurable intelligent surface (RIS)-aided base
station (BS)-centric symbiotic radio (SR) systems. Specifically, the
Internet-of-Things (IoT) devices first sense data from environment and then
tackle the data locally or offload the data to BS for remote computing, while
RISs are leveraged to enhance the quality of blocked channels and also act as
IoT devices to transmit its sensed data. To explore the mechanism of
cooperative sensing and computation offloading in this system, we aim at
maximizing the total completed sensed bits of all users and RISs by jointly
optimizing the time allocation parameter, the passive beamforming at each RIS,
the transmit beamforming at BS, and the energy partition parameters for all
users subject to the size of sensed data, energy supply and given time cycle.
The formulated nonconvex problem is tightly coupled by the time allocation
parameter and involves the mathematical expectations, which cannot be solved
straightly. We use Monte Carlo and fractional programming methods to transform
the nonconvex objective function and then propose an alternating
optimization-based algorithm to find an approximate solution with guaranteed
convergence. Numerical results show that the RIS-aided SR system outperforms
other benchmarks in sensing. Furthermore, with the aid of RIS, the channel and
system performance can be significantly improved.Comment: 13 pages, 7 figure
Distributed Spectrum and Power Allocation for D2D-U Networks: A Scheme based on NN and Federated Learning
In this paper, a Device-to-Device communication on unlicensed bands (D2D-U)
enabled network is studied. To improve the spectrum efficiency (SE) on the
unlicensed bands and fit its distributed structure while ensuring the fairness
among D2D-U links and the harmonious coexistence with WiFi networks, a
distributed joint power and spectrum scheme is proposed. In particular, a
parameter, named as price, is defined, which is updated at each D2D-U pair by a
online trained Neural network (NN) according to the channel state and traffic
load. In addition, the parameters used in the NN are updated by two ways,
unsupervised self-iteration and federated learning, to guarantee the fairness
and harmonious coexistence. Then, a non-convex optimization problem with
respect to the spectrum and power is formulated and solved on each D2D-U link
to maximize its own data rate. Numerical simulation results are demonstrated to
verify the effectiveness of the proposed scheme
Annual Report of the Commission of the Department of Public Utilities for the Year Ending November 30, 1937
Millimeter wave (mmWave) communications provide great potential for
next-generation cellular networks to meet the demands of fast-growing mobile
data traffic with plentiful spectrum available. However, in a mmWave cellular
system, the shadowing and blockage effects lead to the intermittent
connectivity, and the handovers are more frequent. This paper investigates an
``all-mmWave'' cloud radio access network (cloud-RAN), in which both the
fronthaul and the radio access links operate at mmWave. To address the
intermittent transmissions, we allow the mobile users (MUs) to establish
multiple connections to the central unit over the remote radio heads (RRHs).
Specifically, we propose a multipath transmission framework by leveraging the
``all-mmWave'' cloud-RAN architecture, which makes decisions of the RRH
association and the packet transmission scheduling according to the
time-varying network statistics, such that a MU experiences the minimum
queueing delay and packet drops. The joint RRH association and transmission
scheduling problem is formulated as a Markov decision process (MDP). Due to the
problem size, a low-complexity online learning scheme is put forward, which
requires no a priori statistic information of network dynamics. Simulations
show that our proposed scheme outperforms the state-of-art baselines, in terms
of average queue length and average packet dropping rate
- …