A QoS-Aware Interference Balancing Scheme for Multiuser MIMO Systems
ABSTRACT In multiuser MIMO systems, the achievable system data rate as well as quality of service (QoS) of individual users is limited by the inter-user interference (IUI). The interference balancing issue via power allocation can be usually formulated as a nonconvex optimization problem which will become more intractable with nonconvex QoS constraints. In this paper we focus on the challenging QoS-aware optimal power allocation problem, aiming to maximize the system sum rate and guarantee the users' minimum data rates. As a result, a "Polynomial Approximations based on Single Condensation" (PASC) strategy is proposed which transforms the underlying problem to a series of tractable Geometric Programming (GP) problems through single condensation scheme. Extensive simulations have been conducted and the results indicate that PASC is quite suitable to tackle the problem, which can achieve a good balance between the system sum rate and the individual QoS requirements.
- Foundations and Trends in Communications and Information Theory. 01/2005; 2.
Article: Shifting the MIMO Paradigm[show abstract] [hide abstract]
ABSTRACT: Multi-user MIMO (MU-MIMO) networks reveal the unique opportunities arising from a joint optimization of antenna combining techniques with resource allocation protocols. Furthermore, it brings robustness with respect to multipath richness, allowing for compact antenna spacing at the BS and, crucially, yielding the diversity and multiplexing gains without the need for multiple antenna user terminals. To realize these gains, however, the BS should be informed with the user's channel coefficients, which may limit practical application to TDD or low-mobility settings. To circumvent this problem and reduce feedback load, combining MU-MIMO with opportunistic scheduling seems a promising direction. The success for this type of scheduler is strongly traffic and QoS-dependent, however.IEEE Signal Processing Magazine 10/2007; · 3.37 Impact Factor
Article: A tutorial on geometric programming[show abstract] [hide abstract]
ABSTRACT: A geometric program (GP) is a type of mathematical optimization problem characterized by objective and constraint functions that have a special form. Recently developed solution methods can solve even large-scale GPs extremely efficiently and reliably; at the same time a number of practical problems, particularly in circuit design, have been found to be equivalent to (or well approximated by) GPs. Putting these two together, we get effective solutions for the practical problems. The basic approach in GP modeling is to attempt to express a practical problem, such as an engineering analysis or design problem, in GP format. In the best case, this formulation is exact; when this is not possible, we settle for an approximate formulation. This tutorial paper collects together in one place the basic background material needed to do GP modeling. We start with the basic definitions and facts, and some methods used to transform problems into GP format. We show how to recognize functions and problems compatible with GP, and how to approximate functions or data in a form compatible with GP (when this is possible). We give some simple and representative examples, and also describe some common extensions of GP, along with methods for solving (or approximately solving) them.Optimization and Engineering 04/2012; 8(1):67-127. · 0.83 Impact Factor