Multiuser OFDM with adaptive subcarrier, bit, and power allocation
TL;DR: The results show that the proposed algorithm outperforms multiuser OFDM systems with static time-division multiple access (TDMA) or frequency-divisionmultiple access (FDMA) techniques which employ fixed and predetermined time-slot or subcarrier allocation schemes.
Abstract: Multiuser orthogonal frequency division multiplexing (OFDM) with adaptive multiuser subcarrier allocation and adaptive modulation is considered. Assuming knowledge of the instantaneous channel gains for all users, we propose a multiuser OFDM subcarrier, bit, and power allocation algorithm to minimize the total transmit power. This is done by assigning each user a set of subcarriers and by determining the number of bits and the transmit power level for each subcarrier. We obtain the performance of our proposed algorithm in a multiuser frequency selective fading environment for various time delay spread values and various numbers of users. The results show that our proposed algorithm outperforms multiuser OFDM systems with static time-division multiple access (TDMA) or frequency-division multiple access (FDMA) techniques which employ fixed and predetermined time-slot or subcarrier allocation schemes. We have also quantified the improvement in terms of the overall required transmit power, the bit-error rate (BER), or the area of coverage for a given outage probability.
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TL;DR: It is shown that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function, which leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain.
Abstract: The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations
1,634 citations
Cites background from "Multiuser OFDM with adaptive subcar..."
...For nonconvex multiuser spectrum optimization problems, existing approaches in the literature typically focus on either the convex relaxation of the problem [3]–[5] or heuristic methods that approximate the global solution of the problem [6]–[11]....
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...The DSL spectrum balancing problem is very similar to the optimal power allocation and bit-loading problem for OFDM systems in wireless applications [3], [5], [21], [22]....
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TL;DR: A transmit power adaptation method that maximizes the total data rate of multiuser orthogonal frequency division multiplexing (OFDM) systems in a downlink transmission and proposes a simple method where users with the best channel gain for each subcarrier are selected and then the transmit power is equally distributed among the subcarriers.
Abstract: In this paper, we develop a transmit power adaptation method that maximizes the total data rate of multiuser orthogonal frequency division multiplexing (OFDM) systems in a downlink transmission. We generally formulate the data rate maximization problem by allowing that a subcarrier could be shared by multiple users. The transmit power adaptation scheme is derived by solving the maximization problem via two steps: subcarrier assignment for users and power allocation for subcarriers. We have found that the data rate of a multiuser OFDM system is maximized when each subcarrier is assigned to only one user with the best channel gain for that subcarrier and the transmit power is distributed over the subcarriers by the water-filling policy. In order to reduce the computational complexity in calculating water-filling level in the proposed transmit power adaptation method, we also propose a simple method where users with the best channel gain for each subcarrier are selected and then the transmit power is equally distributed among the subcarriers. Results show that the total data rate for the proposed transmit power adaptation methods significantly increases with the number of users owing to the multiuser diversity effects and is greater than that for the conventional frequency-division multiple access (FDMA)-like transmit power adaptation schemes. Furthermore, we have found that the total data rate of the multiuser OFDM system with the proposed transmit power adaptation methods becomes even higher than the capacity of the AWGN channel when the number of users is large enough.
1,393 citations
Cites background or methods from "Multiuser OFDM with adaptive subcar..."
...In [8], the authors attempted to minimize the total transmit power under a fixed performance requirement and a given set of user data rates....
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...However, in the formulation of transmit power minimization problem in [8], they did not allow more than one user to share a subcarrier without any mathematical reasoning....
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...be pointed out that although the subcarrier assignment strategy for users has been found to be the same as the inherent assumption in [8] and [9], we have derived the results from the general formulation including interference from other users’ signals on the same subcarrier by allowing multiple users to share a subcarrier....
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...In the figures, the proposed transmit power adaptation methods (10) and (11) (marked proposed-WFand proposed-EQ, respectively) are compared with the conventional FDMA-like schemes as in [8]....
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...The system model for the problems of transmit power minimization in [8] and minimum capacity maximization in [9] is viewed as a special case of the multiuser OFDM system....
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TL;DR: This paper studies resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-divisionmultiple access (OFDMA), for which the optimal resource allocation is formulated as a mixed-integer problem.
Abstract: Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function , which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.
1,180 citations
Cites background or methods from "Multiuser OFDM with adaptive subcar..."
...One common solution method is relaxationand-rounding, which firstly relaxes the integer constraint ρk,n ∈ {0, 1} as the real-value constraint 0 ≤ ρk,n ≤ 1 [18], and then determines the integer solution using rounding techniques....
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...The corresponding offloading energy consumption can be expressed as below, which is similar to that in [18], namely,...
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..., [18]) and code-division multiple access (CDMA) (see e....
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TL;DR: A set of proportional fairness constraints is imposed to assure that each user can achieve a required data rate, as in a system with quality of service guarantees, and a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation is proposed.
Abstract: Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless local area network (LAN) systems. The sum capacity of MU-OFDM is maximized when each subchannel is assigned to the user with the best channel-to-noise ratio for that subchannel, with power subsequently distributed by water-filling. However, fairness among the users cannot generally be achieved with such a scheme. In this paper, a set of proportional fairness constraints is imposed to assure that each user can achieve a required data rate, as in a system with quality of service guarantees. Since the optimal solution to the constrained fairness problem is extremely computationally complex to obtain, a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation is proposed. In the proposed algorithm, subchannel allocation is first performed by assuming an equal power distribution. An optimal power allocation algorithm then maximizes the sum capacity while maintaining proportional fairness. The proposed algorithm is shown to achieve about 95% of the optimal capacity in a two-user system, while reducing the complexity from exponential to linear in the number of subchannels. It is also shown that with the proposed resource allocation algorithm, the sum capacity is distributed more fairly and flexibly among users than the sum capacity maximization method.
1,084 citations
TL;DR: These technologies such as multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), cognitive radio, network coding, cooperative communication, etc.
Abstract: Reducing energy consumption in wireless communications has attracted increasing attention recently. Advanced physical layer techniques such as multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), cognitive radio, network coding, cooperative communication, etc.; new network architectures such as heterogeneous networks, distributed antennas, multi-hop cellulars, etc.; as well as radio and network resource management schemes such as various cross-layer optimization algorithms, dynamic power saving, multiple radio access technologies coordination, etc. have been proposed to address this issue. In this article, we overview these technologies and present the state-of-the-art on each aspect. Some challenges that need to be solved in the area are also described.
954 citations
References
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Book•
15 Jan 1996
TL;DR: WireWireless Communications: Principles and Practice, Second Edition is the definitive modern text for wireless communications technology and system design as discussed by the authors, which covers the fundamental issues impacting all wireless networks and reviews virtually every important new wireless standard and technological development, offering especially comprehensive coverage of the 3G systems and wireless local area networks (WLANs).
Abstract: From the Publisher:
The indispensable guide to wireless communicationsnow fully revised and updated!
Wireless Communications: Principles and Practice, Second Edition is the definitive modern text for wireless communications technology and system design. Building on his classic first edition, Theodore S. Rappaport covers the fundamental issues impacting all wireless networks and reviews virtually every important new wireless standard and technological development, offering especially comprehensive coverage of the 3G systems and wireless local area networks (WLANs) that will transform communications in the coming years. Rappaport illustrates each key concept with practical examples, thoroughly explained and solved step by step.
Coverage includes:
An overview of key wireless technologies: voice, data, cordless, paging, fixed and mobile broadband wireless systems, and beyond
Wireless system design fundamentals: channel assignment, handoffs, trunking efficiency, interference, frequency reuse, capacity planning, large-scale fading, and more
Path loss, small-scale fading, multipath, reflection, diffraction, scattering, shadowing, spatial-temporal channel modeling, and microcell/indoor propagation
Modulation, equalization, diversity, channel coding, and speech coding
New wireless LAN technologies: IEEE 802.11a/b, HIPERLAN, BRAN, and other alternatives
New 3G air interface standards, including W-CDMA, cdma2000, GPRS, UMTS, and EDGE
Bluetooth wearable computers, fixed wireless and Local Multipoint Distribution Service (LMDS), and other advanced technologies
Updated glossary of abbreviations and acronyms, and a thorolist of references
Dozens of new examples and end-of-chapter problems
Whether you're a communications/network professional, manager, researcher, or student, Wireless Communications: Principles and Practice, Second Edition gives you an in-depth understanding of the state of the art in wireless technologytoday's and tomorrow's.
17,102 citations
01 Nov 1985
TL;DR: This month's guest columnist, Steve Bible, N7HPR, is completing a master’s degree in computer science at the Naval Postgraduate School in Monterey, California, and his research area closely follows his interest in amateur radio.
Abstract: Spread Spectrum It’s not just for breakfast anymore! Don't blame me, the title is the work of this month's guest columnist, Steve Bible, N7HPR (n7hpr@tapr.org). While cruising the net recently, I noticed a sudden bump in the number of times Spread Spectrum (SS) techniques were mentioned in the amateur digital areas. While QEX has discussed SS in the past, we haven't touched on it in this forum. Steve was a frequent cogent contributor, so I asked him to give us some background. Steve enlisted in the Navy in 1977 and became a Data Systems Technician, a repairman of shipboard computer systems. In 1985 he was accepted into the Navy’s Enlisted Commissioning Program and attended the University of Utah where he studied computer science. Upon graduation in 1988 he was commissioned an Ensign and entered Nuclear Power School. His subsequent assignment was onboard the USS Georgia, a trident submarine stationed in Bangor, Washington. Today Steve is a Lieutenant and he is completing a master’s degree in computer science at the Naval Postgraduate School in Monterey, California. His areas of interest are digital communications, amateur satellites, VHF/UHF contesting, and QRP. His research area closely follows his interest in amateur radio. His thesis topic is Multihop Packet Radio Routing Protocol Using Dynamic Power Control. Steve is also the AMSAT Area Coordinator for the Monterey Bay area. Here's Steve, I'll have some additional comments at the end.
8,781 citations
Book•
01 Jan 1968
TL;DR: This book shows engineers how to use optimization theory to solve complex problems with a minimum of mathematics and unifies the large field of optimization with a few geometric principles.
Abstract: From the Publisher:
Engineers must make decisions regarding the distribution of expensive resources in a manner that will be economically beneficial. This problem can be realistically formulated and logically analyzed with optimization theory. This book shows engineers how to use optimization theory to solve complex problems. Unifies the large field of optimization with a few geometric principles. Covers functional analysis with a minimum of mathematics. Contains problems that relate to the applications in the book.
5,667 citations
"Multiuser OFDM with adaptive subcar..." refers methods in this paper
...Using standard optimization techniques in [ 17 ], we obtain the Lagrangian...
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