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Resource Allocation and Node Placement in Multi-Hop Heterogeneous Integrated-Access-and-Backhaul Networks
(2020-01-01)
Lai, Jiun Y.
;
Wu, Wu-Hsiu
;
Su, Yu T.
;
電信工程研究所
;
Institute of Communications Engineering
We consider a heterogeneous MIMO-OFDMA based dense small cell (SC) system in which each macro cell base station (MBS) serves its coverage area with the help of small cell base stations (SBSs) through multi-hop wireless connections. The SBSs act as integrated access and backhaul (IAB) nodes that handle both access and backhaul traffics with wireless links. We first develop an optimal (sum-rate maximization) resource allocation (RA) algorithm which considers subcarriers/spatial subchannels assignment and the associated power allocations. We also present two low-complexity suboptimal RA schemes which, as verified by simulations, incur only minor performance loss in the high SNR region. Our RA algorithms can be applied to other multi-hop networks with general UE association rule and node location distributions. We study the channel aging effect caused by the time lag between the time channel state information (CSI) is measured and that when data transmission occurs. We show the benefit of channel prediction and the limit of a centralized RA approach. The advantages of frequency (channel) reuse and the multi-hop architecture are demonstrated as well. A related but perhaps more important system design issue for an IAB cellular network is the IAB node placement problem. With the given UE association rule and UE location distribution, we present systematic approaches to find the optimal node locations. For two special propagation models, we derive closed-form expressions for the node locations that maximizes a spectral efficiency lower bound. Numerical results validate the accuracy of our estimates based on either numerical evaluations or closed-form solutions.
Pair Auction and Matching for Resource Allocation in Full-Duplex Cellular Systems
(2020-04-01)
Chao, Cheng-Chih
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Wang, Chih-Yu
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Lee, Chia-Han
;
Wei, Hung-Yu
;
Chen, Wen-Tsuen
Full-duplex techniques improve spectral efficiency in cellular networks by allowing a transceiver to simultaneously transmit and receive data with the same radio resource. To minimize self-interference and inter-node interference due to the deployment of full-duplex radio, users have to feed back their measurements of interference to allow the base station to effectively allocate resources and form transmission pairs. Considering that the interference measurements are private information, users may not report the true values and may try to gain their own benefits. To solve this problem, we propose a strategy-proof resource allocation mechanism for full-duplex cellular networks. The proposed scheme, which is based on auction and matching theories, guarantees that users report true values of their channel quality and utilities, and therefore guarantees the correctness of feedback information and the efficiency of resource allocation. Moreover, the proposed matching-based mechanism is theoretically proved to be stable and Pareto efficient. Simulation results confirm that, compared to a centralized scheme with non-truthful information from users, the proposed scheme can achieve an 11% gain.
Joint Interference Cancellation and Resource Allocation for Full-Duplex Cloud Radio Access Networks
(2019-06-01)
Fang, Chun-Hao
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Li, Pei-Rong
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Feng, Kai-Ten
;
電機工程學系
;
Department of Electrical and Computer Engineering
In this paper, we study joint interference cancellation and resource allocation for full-duplex (FD) cloud radio access networks (C-RANs) with antenna correlation. The target is to maximize the capacity of downlink (DL) user equipments (UEs) with guaranteed quality of service (QoS) of uplink (UL) UEs under the constraints of DL and UL transmit power. Intractability of the considered problem involves non-convexity and coupling between postcoding and remote radio head (RRH) selection. To deal with the high coupling between system variables, an estimation-free self-interference (SI) cancellation (EFSC) scheme with merit of significantly reducing signaling overhead for channel estimation is proposed. The reduced signaling overhead is also derived in closed-form expression. Furthermore, we propose a generalized Bender's decomposition-based resource allocation (GRA) algorithm, which separates the continuous and discrete variables to solve the optimization problem. With the design of a flexible utility function, the tradeoff between DL capacity and co-channel interference (CCI) can be achieved. Moreover, we identify the scenario in which antenna correlation will be beneficial for both the UL and DL communications in FD C-RAN with the implementation of EFSC scheme. The effectiveness of the proposed methods and theorems are verified via simulation results.
Decentralized Auctioneerless Combinatorial Auctions for Multi-Unit Resource Allocation
(2019-01-01)
Yen, Li-Hsing
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Sun, Guang-Hong
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資訊工程學系
;
Department of Computer Science
Auction has been used to allocate resources or tasks to processes, machines, or other autonomous agents in distributed systems. Among various types of auctions, combinatorial auction (CA) allocates a bundle of items to each agent at once. Finding an optimal auction result for CA that maximizes total winning bid is NP-hard. Many time-efficient approximations to this problem work with a bid ranking function (BRF). However, the existing approximations are mostly for single-unit resource and demand an auctioneer. This paper proposes the first auctioneerless open-bid multi-unit CA (MUCA) scheme. It includes a BRF-based winner determination scheme that enables every agent to locally compute a critical bid value for it to win the MUCA and accordingly take its best response to other agent's bid and win declarations. It also allows each winner to locally compute its payment for a critical-value-based pricing scheme. We analyze stabilization, correctness, and consistency properties of the proposed approach. The simulation results confirm that the proposed approach identifies exactly the same set of winners as the centralized counterpart regardless of initial bid setting, but at the cost of the lower total winning bid and payment.
GGRA: A Feasible Resource Allocation Scheme by Optimization Technique for IEEE 802.16 Uplink Systems
(2010-01-01)
Chang, Chung-Ju
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Chiu, Yin
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Feng, Kai-Ten
;
Ren, Fang-Ching
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電信工程研究所
Generally, optimization techniques for resource allocation of OFDMA systems are infeasible for real-time applications. In this paper, we propose a genetic algorithm with subscriber station (SS) grouping resource allocation (GGRA) scheme for IEEE 802.16 uplink systems. The GGRA scheme firstly designs a rate assignment strategy, applied with a predefined residual lifetime, to allocate resource to each service dynamically. It then aggregates high correlation SSs into the same group, where the SSs will be allocated to different slots so as to avoid mutual user interference. Finally, the GGRA scheme finds an optimal assignment matrix for the system by the genetic algorithm, based on the SS groups to greatly lessen the computation complexity. The GGRA scheme can also maximize system throughput and fulfill QoS requirements. Simulation results show that the proposed GGRA scheme performs better than the EFS algorithm [6] and the MLWDF algorithm [7] in system throughput, voice/video packet drop rate, unsatisfied ratio of HTTP users/packets, and FTP throughput. The computation complexity of the GGRA scheme is also tractable and thus feasible for real-time applications.
Toward Optimal Resource Allocation of Virtualized Network Functions for Hierarchical Datacenters
(2018-12-01)
Wang, Chih-Chiang
;
Lin, Ying-Dar
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Wu, Jang-Jiin
;
Lin, Po-Ching
;
Hwang, Ren-Hung
Telecommunications service providers (TSPs) previously provided network functions to end users with dedicated hardware, but they are resorting to virtualized infrastructure for reducing costs and increasing flexibility in resource allocation. A representative case is the Central Office Re-architected as Datacenter (CORD) project from AT&T, which aims to deploy virtualized network functions (VNFs) to over 4000 central offices (COs) across the U.S. However, there is a wide spectrum of options for deploying VNFs over the COs, varying from highly distributed to highly centralized manners. The former benefits end users with short response time but has its inherent limitation on utilizing geographically dispersed resources, while the latter allows resources to be better utilized at a cost of longer response time. In this work, we model the TSP's virtualized infrastructure as hierarchical datacenters, namely hierarchical CORD, and provide a resource allocation solution to strike the optimal balance between the two extreme options. Our evaluations reveal that in general, the 3-tier architecture incurs the least cost in case of deploying VNFs under moderate or loose delay constraints. Furthermore, the margin of improvement on the resource allocation cost increases inversely with the overall system utilization rate. Our results also suggest that as heavy request load overwhelms the network infrastructure, the relevant VNFs shall be migrated to lower-tier edge datacenters or to some nearby datacenters with superior network capacity. The evaluations also demonstrate that the proposed model allows highly adaptive VNF deployment in the hierarchical architecture under various conditions.
SOLVING THE OPTIMAL RESOURCE ALLOCATION IN MULTIMODAL STOCHASTIC ACTIVITY NETWORKS USING AN OPTIMAL COMPUTING BUDGET ALLOCATION TECHNIQUE
(2018-01-01)
Lin, Jen-Yen
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Yao, Ming-Jong
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Chu, Yi-Hua
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
The problem of optimal Resource Allocation in Multimodal Stochastic Activity Networks (RAMSAN) has been studied for more than a decade. Many researchers proposed solution approaches, including dynamic programming and meta-heuristics, for solving the RAMSAN, and they commonly applied Monte Carlo Simulation (MCS) for evaluating the objective function values for the candidate solutions. However, since the computational load of MCS is very demanding, the solution approaches using MCS become impractical, even for only medium-size problems. In this study, we propose a Genetic Algorithm (GA) with an Optimal Computing Budget Allocation (OCBA) approach for solving the RAMSAN. We utilize the technique of OCBA to optimally allocate the computational budget among the candidate solutions in the evolutionary process of GA for evaluating their objective functions. Based on the benchmark instances in the literature, we demonstrate the proposed GA with OCBA is more effective than the other solution approaches in the literature and a GA without using OCBA.
Joint Resource Allocation for Multi-homing and Single-network Users in Heterogeneous Cognitive Radio Networks
(2017-01-01)
Chen, Feng
;
Fu, Mingjian
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Jia, Wen-Kang
;
交大名義發表
;
National Chiao Tung University
This paper investigates the resource allocation problem in heterogeneous cognitive radio networks (HCRNs). In this paper, two types of users, i.e., multi-homing and single-network cognitive radio users (CRUs), are considered to coexist in HCRNs. Multi-homing CRUs can access multiple networks simultaneously, while single-network CRUs can be served by only one network at a time. With the aim of maximizing the system capacity of HCRNs, the resource allocation problem is formulated as a joint multi-dimension optimization problem which includes network selection, bandwidth and power allocation for both multi-homing and single-network CRUs. However, the joint multi-dimension optimization leads the problem to be non-convex and NP-hard. To solve the problem efficiently, continuous relaxation is performed firstly, and next a near-optimal solution using Lagrangian dual decomposition and Karush-Kuhn-Tucker (KKT) conditions is proposed in this paper. Numerical results show the proposed algorithm improves the system capacity significantly compared to the traditional ones.
A DEA benchmarking methodology for strategic management of new product development based on decentralized profit-center business model
(2007-01-01)
Trappey, Amy J. C.
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Chiang, Tzu-An
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Wu, Hsin-Ying
;
Hou, Eric J. -R.
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管理科學系
In today's highly competitive marketplace, technology-driven large organizations widely adopt decentralized profit-center business model. In order to complete a series of NPD activities on time and in budget, the profit-center managers need an objective benchmarking approaches to gain precious perception into the relation of different resource allocations, profits, costs and times for each NPD activity. Thus, this study put forward a novel benchmarking methodology to optimize the NPD activities within a profit center for achieving the maximum profit goal and to guide NPD engineers to learn the best benchmarking practices. By applying the real case of the electric scooter design project, this research demonstrates the methodology at work with superior results, comparing to other existing approaches.
Spatial and Temporal Aggregation for Small and Massive Transmissions in LTE-M Networks
(2017-01-01)
Chang, Po-Yen
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Liang, Jia-Ming
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Chen, Jen-Jee
;
Wu, Kun-Ru
;
Tseng, Yu-Chee
Machine-to-machine (M2M) communication is one of the key technologies to realize Internet of Things (IoT). Since IoT applications are mainly for smart sensing, such as metering, home surveillance, disaster detection, and e-health, their special sensing/uploading behaviors will result in periodic and/or eventdriven small data transmissions, which may potentially decrease the radio resource efficiency. On the other hand, the widespread deployment of IoT raises the concurrent massive connectivity of IoT devices. How to solve these two problems is a critical issue. In this paper, we investigate an uplink resource allocation problem which considers the periodic, event-driven, and query-based IoT traffic behaviors over LTE-M. The proposed approach takes advantage of data aggregation and both spatial and temporal reuse. Our solution exploits long-term static scheduling for periodic data to ensure the latency and data rate, and employs short-term dynamic scheduling for event-driven, query-based data to improve transmission efficiency. Therefore, both small data and massive connectivity problems are relieved. Extensive simulation results show that the proposed scheme can improve resource efficiency and enlarge network capacity effectively.

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