As of today, orthogonal frequency division multiplexing (OFDM) has been the dominant technology for broadband multicarrier communications. However, in certain applications such as cognitive radios and uplink of multiuser multicarrier systems, where a subset of subcarriers is allocated to each user, OFDM may be an undesirable solution. In this article, we address the shortcomings of OFDM in these and other applications and show that filter bank multicarrier (FBMC) could be a more effective solution. Although FBMC methods have been studied by a number of researchers, and some even before the invention of OFDM, only recently has FBMC been seriously considered by a few standard committees.

OFDM Versus Filter Bank Multicarrier

The mutual information of independent parallel Gaussian-noise channels is maximized, under an average power constraint, by independent Gaussian inputs whose power is allocated according to the waterfilling policy. In practice, discrete signaling constellations with limited peak-to-average ratios (m-PSK, m-QAM, etc.) are used in lieu of the ideal Gaussian signals. This paper gives the power allocation policy that maximizes the mutual information over parallel channels with arbitrary input distributions. Such policy admits a graphical interpretation, referred to as mercury/waterfilling, which generalizes the waterfilling solution and allows retaining some of its intuition. The relationship between mutual information of Gaussian channels and nonlinear minimum mean-square error (MMSE) proves key to solving the power allocation problem.

/pdf/optimum-power-allocation-for-parallel-gaussian-channels-with-6fj5ovds2s.pdf

Optimum power allocation for parallel Gaussian channels with arbitrary input distributions

This paper proposes efficient rate and power allocation algorithms for OFDMA downlink systems where each tone is taken by at most one user. Weighted sum rate maximization (WSRmax) and weighted sum power minimization (WSPmin) problems are considered. Since these resource allocation problems are non-convex, complexity of finding the optimal solutions is prohibitively high. This paper employs the Lagrange dual decomposition method to efficiently solve both optimization problems. Because of their non-convex nature, there is no guarantee for the solution obtained by the dual decomposition method to be optimal. However, it is shown that with practical number of tones, the duality gap is virtually zero and the optimal solutions can be efficiently obtained.

Optimal Resource Allocation for OFDMA Downlink Systems

Many engineering problems that can be formulated as constrained optimization problems result in solutions given by a waterfilling structure; the classical example is the capacity-achieving solution for a frequency-selective channel. For simple waterfilling solutions with a single waterlevel and a single constraint (typically, a power constraint), some algorithms have been proposed in the literature to compute the solutions numerically. However, some other optimization problems result in significantly more complicated waterfilling solutions that include multiple waterlevels and multiple constraints. For such cases, it may still be possible to obtain practical algorithms to evaluate the solutions numerically but only after a painstaking inspection of the specific waterfilling structure. In addition, a unified view of the different types of waterfilling solutions and the corresponding practical algorithms is missing. The purpose of this paper is twofold. On the one hand, it overviews the waterfilling results existing in the literature from a unified viewpoint. On the other hand, it bridges the gap between a wide family of waterfilling solutions and their efficient implementation in practice; to be more precise, it provides a practical algorithm to evaluate numerically a general waterfilling solution, which includes the currently existing waterfilling solutions and others that may possibly appear in future problems.

Practical algorithms for a family of waterfilling solutions

Dynamic spectrum management of digital subscriber lines (DSLs) has the potential to dramatically increase the capacity of the aging last-mile copper access network. This paper takes an important step toward fulfilling this potential through power spectrum balancing. We derive a novel algorithm called SCALE, that provides a significant performance improvement over the existing iterative water-filling (IWF) algorithm in multiuser DSL networks, doing so with comparable low complexity. The algorithm is easily distributed through measurement and limited message passing with the use of a spectrum management center. We outline how overhead can be managed, and show that in the limit of zero message-passing, performance reduces to IWF.

/pdf/scale-a-low-complexity-distributed-protocol-for-spectrum-4r1byvafev.pdf

SCALE: A Low-Complexity Distributed Protocol for Spectrum Balancing in Multiuser DSL Networks

Cutting-edge xDSL: technology, standards, architecture, regulation, and its application. The state of the art in DSL technology, standards, and architecture ADSL, HDSL2, SHDSL, VDSL, line unbundling, spectrum management, and more Voice over DSL and video-on-demand: technical progress and its impact on services DSL in the home: automating deployment, enhancing security, maximizing revenue Improved coding techniques, multi-user detection, and other new advancesDigital Subscriber Lines (DSLs) have transformed millions of ordinary phone lines into broadband arteries that link homes and businesses to the Internet at megabit speeds. DSL Advances brings together the state of the art in DSL technology and architecture for every technical professional and manager. The authors of the classic Understanding Digital Subscriber Line Technology review the key challenges service providers and equipment manufacturers face now, preview tomorrow's most important standards and technical enhancements, and offer new insights into today's regulatory and business environment. Coverage includes: HDSL2 and SHDSL: The next generation of symmetric DSL technology Recent developments in ADSL and standards for line unbundling Spectrum management standards: "traffic cops" for crosstalk management Voice over DSL and video-on-demand: technical progress that enhances the business case DSL networked homes: wired/wireless LANs, shared phone and AC wiring?including both centralized splitters and distributed in-line filter premises wiring Streamlining deployment: CPE auto-configuration and flow-through service provisioning Security risks and essential safeguards for vendors, service providers, and users Future directions, including advanced coding techniques and multi-user detection

DSL Advances

Digital subscriber lines

Operator-controlled implementations of user preferences are provided when feasible. User preference data is obtained by the operator and compared to operational characteristics and parameters of a communication system, such as a DSL system, to determine whether one or more of the user preferences can be implemented in the communication system. When implementation of a user preference would violate operational rules of the system, or where implementation would adversely affect system operation, the preference need not be implemented. However, when a user preference can be implemented in the system without causing problems, the operator can implement (or permit another party to implement) the user preference to effect the user's desires. The user preference data can be obtained directly from users (for example, by surveys and other direct user feedback) or can be obtained indirectly (for example, by constructing a Hidden Markov Model that shows user preferences). The operator may collect the user preference data from a user set (for example, a single user or a plurality of users). The user preference data can be compared to 2 or more performance metrics that can be adjusted, to the extent feasible, to implement the user preference data.

User-preference-based dsl system

An apparatus comprises at least one vectoring engine (1511; 1512) and a cross-connect (1520) coupled to the vectoring engine (1511; 1512). The cross-connect (1520) is to couple with each of a plurality of customer premises equipment (CPE) devices (1541; 1592; 1543; 1544) via a respective DSL loop (1531; 1532; 1533; 1534). An interface is to receive instructions for the cross-connect (1520) to couple the vectoring engine (1511) to a nonoverlapping subset of the CPE devices (1541; 1543) via the respective DSL loops (1532,- 1533). The interface is further to receive instructions for the vectoring engine (1511) to apply vectoring to the DSL loop via which to couple one of the CPE devices to the cross-connect.

Vectored dsl system

A Digital Subscriber Line (DSL) Management Center (DMC) coupled to a DSL network includes a data collection module that receives information regarding the DSL network from a plurality of sources. An analysis module is coupled to the data collection module to analyze the received information and issue a command for one or more of a plurality of DSL performance enhancement devices to optimize their operation. A command signal generation module is coupled to the analysis module to receive the issued command from the analysis module and generate a corresponding command signal for transmission to one or more of the DSL performance enhancement devices.

Method and apparatus for DSL performance enhancement in a DSL network

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