This paper studies robust transceiver optimization for cognitive downlink systems in the presence of imperfect channel state information (CSI). We aim at minimizing the sum mean square error (MSE) of the secondary downlink transmission subject to the interference constraint imposed on the primary users. The MSE duality is developed to describe the uplink-downlink relation, in which imperfect CSI and multiple power constraints are taken into account. Based on the duality result, we propose an efficient robust approach which can effectively avoid the violation of the interference constraint. We also discuss the performance in terms of optimality as well as complexity issue. Compared to the downlink-based approach, the proposed one features faster convergence speed and lower complexity.
[Show abstract][Hide abstract] ABSTRACT: We define a duality between Gaussian multiple-access channels (MACs) and Gaussian broadcast channels (BCs). The dual channels we consider have the same channel gains and the same noise power at all receivers. We show that the capacity region of the BC (both constant and fading) can be written in terms of the capacity region of the dual MAC, and vice versa. We can use this result to find the capacity region of the MAC if the capacity region of only the BC is known, and vice versa. For fading channels we show duality under ergodic capacity, but duality also holds for different capacity definitions for fading channels such as outage capacity and minimum-rate capacity. Using duality, many results known for only one of the two channels can be extended to the dual channel as well.
IEEE Transactions on Information Theory 06/2004; 50(5-50):768 - 783. DOI:10.1109/TIT.2004.826646 · 2.33 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Owing to the structure of the Gaussian multiple-input multiple-output
(MIMO) broadcast channel (BC), associated optimization problems such as
capacity region computation and beamforming optimization are typically
non-convex, and cannot be solved directly. One feasible approach to
these problems is to transform them into their dual multiple access
channel (MAC) problems, which are easier to deal with due to their
convexity properties. The conventional BC-MAC duality is established via
BC-MAC signal transformation, and has been successfully applied to solve
beamforming optimization, signal-to-interference-plus-noise ratio (SINR)
balancing, and capacity region computation. However, this conventional
duality approach is applicable only to the case, in which the base
station (BS) of the BC is subject to a single sum power constraint. An
alternative approach is minimax duality, established by Yu in the
framework of Lagrange duality, which can be applied to solve the
per-antenna power constraint problem. This paper extends the
conventional BC-MAC duality to the general linear constraint case, and
thereby establishes a general BC-MAC duality. This new duality is
applied to solve the capacity computation and beamforming optimization
for the MIMO and multiple-input single-output (MISO) BC, respectively,
with multiple linear constraints. Moreover, the relationship between
this new general BC-MAC duality and minimax duality is also presented.
It is shown that the general BC-MAC duality offers more flexibility in
solving BC optimization problems relative to minimax duality. Numerical
results are provided to illustrate the effectiveness of the proposed
IEEE Transactions on Information Theory 01/2008; abs/0809.4101(4). DOI:10.1109/ISIT.2009.5206059 · 2.33 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Cognitive radios hold tremendous promise for increasing spectral efficiency in wireless systems. This paper surveys the fundamental capacity limits and associated transmission techniques for different wireless network design paradigms based on this promising technology. These paradigms are unified by the definition of a cognitive radio as an intelligent wireless communication device that exploits side information about its environment to improve spectrum utilization. This side information typically comprises knowledge about the activity, channels, codebooks, and/or messages of other nodes with which the cognitive node shares the spectrum. Based on the nature of the available side information as well as a priori rules about spectrum usage, cognitive radio systems seek to underlay, overlay, or interweave the cognitive radios' signals with the transmissions of noncognitive nodes. We provide a comprehensive summary of the known capacity characterizations in terms of upper and lower bounds for each of these three approaches. The increase in system degrees of freedom obtained through cognitive radios is also illuminated. This information-theoretic survey provides guidelines for the spectral efficiency gains possible through cognitive radios, as well as practical design ideas to mitigate the coexistence challenges in today's crowded spectrum.
Proceedings of the IEEE 06/2009; 97(5-97):894 - 914. DOI:10.1109/JPROC.2009.2015717 · 4.93 Impact Factor
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