1,099 research outputs found
On the Secrecy Capacity of Fading Channels
We consider the secure transmission of information over an ergodic fading
channel in the presence of an eavesdropper. Our eavesdropper can be viewed as
the wireless counterpart of Wyner's wiretapper. The secrecy capacity of such a
system is characterized under the assumption of asymptotically long coherence
intervals. We first consider the full Channel State Information (CSI) case,
where the transmitter has access to the channel gains of the legitimate
receiver and the eavesdropper. The secrecy capacity under this full CSI
assumption serves as an upper bound for the secrecy capacity when only the CSI
of the legitimate receiver is known at the transmitter, which is characterized
next. In each scenario, the perfect secrecy capacity is obtained along with the
optimal power and rate allocation strategies. We then propose a low-complexity
on/off power allocation strategy that achieves near-optimal performance with
only the main channel CSI. More specifically, this scheme is shown to be
asymptotically optimal as the average SNR goes to infinity, and interestingly,
is shown to attain the secrecy capacity under the full CSI assumption.
Remarkably, our results reveal the positive impact of fading on the secrecy
capacity and establish the critical role of rate adaptation, based on the main
channel CSI, in facilitating secure communications over slow fading channels.Comment: 18 pages, 3 figures, Submitted to the IEEE Trans. on Information
Theor
Transmit without regrets: Online optimization in MIMO-OFDM cognitive radio systems
In this paper, we examine cognitive radio systems that evolve dynamically
over time due to changing user and environmental conditions. To combine the
advantages of orthogonal frequency division multiplexing (OFDM) and
multiple-input, multiple-output (MIMO) technologies, we consider a MIMO-OFDM
cognitive radio network where wireless users with multiple antennas communicate
over several non-interfering frequency bands. As the network's primary users
(PUs) come and go in the system, the communication environment changes
constantly (and, in many cases, randomly). Accordingly, the network's
unlicensed, secondary users (SUs) must adapt their transmit profiles "on the
fly" in order to maximize their data rate in a rapidly evolving environment
over which they have no control. In this dynamic setting, static solution
concepts (such as Nash equilibrium) are no longer relevant, so we focus on
dynamic transmit policies that lead to no regret: specifically, we consider
policies that perform at least as well as (and typically outperform) even the
best fixed transmit profile in hindsight. Drawing on the method of matrix
exponential learning and online mirror descent techniques, we derive a
no-regret transmit policy for the system's SUs which relies only on local
channel state information (CSI). Using this method, the system's SUs are able
to track their individually evolving optimum transmit profiles remarkably well,
even under rapidly (and randomly) changing conditions. Importantly, the
proposed augmented exponential learning (AXL) policy leads to no regret even if
the SUs' channel measurements are subject to arbitrarily large observation
errors (the imperfect CSI case), thus ensuring the method's robustness in the
presence of uncertainties.Comment: 25 pages, 3 figures, to appear in the IEEE Journal on Selected Areas
in Communication
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