Linear programming and other classical optimization techniques have found important applications in communication systems for several decades. Recently, there has been a surge in research activities that utilize the power of recent developments in nonlinear optimization to tackle a much wider scope of work in the analysis and design of communication systems, touching every "layer" of the layered network architecture, and resulting in intellectual and practical impacts significantly beyond the established frameworks in the early 1990s. These research activities are driven by both new demands in the areas of communications and networking, and new tools emerging from optimization theory. Such tools include new developments of powerful theories and highly efficient computational algorithms for nonlinear convex optimization, as well as global solution methods and relaxation techniques for nonconvex optimization.

The phrase "nonlinear optimization of communication systems" carries at least three different meanings. In the most straightforward way, an analysis or design problem in a communication system may be formulated as either minimizing a cost or maximizing a utility function over a set of variables confined within a constraint set. In a more subtle and recent approach, a given network protocol may be interpreted as a distributed algorithm solving an implicit, global optimization problem. In yet another approach, the underlying theory of a network control method or a communication strategy may be generalized using nonlinear optimization techniques, thus extending the scope of applicability of the theory. Over the last few years, the nonlinear optimization framework has been successfully applied to a wide range of communication sytems, from the high speed Internet core to wireless networks, from coding and equalization to broadband access, and from information theory to network topology models.

We are soliciting papers on the topics of the applications of the theories and algorithms of nonlinear optimization (convex or nonconvex) and Lagrange duality to various aspects of communication systems analysis and design, including parallel and distributed optimization techniques for problems where the problem data or control mechanisms are spatially distributed.

• Networking protocols as distributed optimization algorithms:
  • Internet protocols such as TCP/AQM/IP
  • Wireless network protocols such as medium access and routing
  • Allocation of network resources such as bandwidth, rate, or power
  • Queuing systems performance optimization
  • Optimization-based models of network traffic and topology
• Digital communications signal processing algorithms via nonlinear optimization methods:
  • Signal processing algorithms such as equalization, precoding, modulation, shaping, and channel estimation
  • Wireless transceiver design
  • Wireless MIMO systems design
  • Power allocation and spectrum management in DSL
  • Multiuser detection algorithms
• Information-theoretic limits obtained by nonlinear optimization:
  • Characterization and computation of channel capacity and rate distortion
  • Multi-terminal channel capacity and data compression problems
  • Information rates in channels with memory
  • Channel and source code optimization
  • Generalization and application of waterfilling algorithms
• Cross-layer design through nonlinear optimization decomposition:
  • TCP/IP, TCP/MAC, TCP/PHY interactions
  • Scheduling based on wireless fading environment
  • Other cross-layer problems formulated through the framework of nonlinear optimization
  • Topics that involve more than one of the three main topics above: networking, physical layer signal processing, and information theory, in the nonlinear optimization frameworks

Prospective authors should follow the IEEE J-SAC manuscript format described in the Information for Authors. Only original and unpublished papers will be considered. Authors are encouraged to include adequate mathematical background materials in their papers to make the results and methodologies accessible to a wide range of J-SAC readers.

All papers should be submitted in PDF format via email to Mung Chiang, chiangm@princeton.edu, according to the following timetable:

Guest Editors:

• Mung Chiang, Electrical Engineering Dept, Princeton University, USA, chiangm@princeton.edu
• Steven Low, Computer Science & Electrical Engineering Dept, California Institute of Technology, USA, slow@caltech.edu
• Tom Zhi-Quan Luo, Electrical & Computer Engineering Dept, University of Minnesota, USA, luozq@ece.umn.edu
• Ness Shroff, Electrical & Computer Engineering Dept, Purdue University, USA, shroff@ecn.purdue.edu
• Wei Yu, Electrical & Computer Engineering Dept, University of Toronoto, Canada, weiyu@comm.utoronto.ca