9 research outputs found
Secrecy Energy Efficiency of MIMOME Wiretap Channels with Full-Duplex Jamming
Full-duplex (FD) jamming transceivers are recently shown to enhance the
information security of wireless communication systems by simultaneously
transmitting artificial noise (AN) while receiving information. In this work,
we investigate if FD jamming can also improve the systems secrecy energy
efficiency (SEE) in terms of securely communicated bits-per- Joule, when
considering the additional power used for jamming and self-interference (SI)
cancellation. Moreover, the degrading effect of the residual SI is also taken
into account. In this regard, we formulate a set of SEE maximization problems
for a FD multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME)
wiretap channel, considering both cases where exact or statistical channel
state information (CSI) is available. Due to the intractable problem structure,
we propose iterative solutions in each case with a proven convergence to a
stationary point. Numerical simulations indicate only a marginal SEE gain,
through the utilization of FD jamming, for a wide range of system conditions.
However, when SI can efficiently be mitigated, the observed gain is
considerable for scenarios with a small distance between the FD node and the
eavesdropper, a high Signal-to-noise ratio (SNR), or for a bidirectional FD
communication setup.Comment: IEEE Transactions on Communication
Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems
Data compression has the potential to significantly improve the computation
offloading performance in hierarchical fog-cloud systems. However, it remains
unknown how to optimally determine the compression ratio jointly with the
computation offloading decisions and the resource allocation. This joint
optimization problem is studied in the current paper where we aim to minimize
the maximum weighted energy and service delay cost (WEDC) of all users. First,
we consider a scenario where data compression is performed only at the mobile
users. We prove that the optimal offloading decisions have a threshold
structure. Moreover, a novel three-step approach employing convexification
techniques is developed to optimize the compression ratios and the resource
allocation. Then, we address the more general design where data compression is
performed at both the mobile users and the fog server. We propose three
efficient algorithms to overcome the strong coupling between the offloading
decisions and resource allocation. We show that the proposed optimal algorithm
for data compression at only the mobile users can reduce the WEDC by a few
hundred percent compared to computation offloading strategies that do not
leverage data compression or use sub-optimal optimization approaches. Besides,
the proposed algorithms for additional data compression at the fog server can
further reduce the WEDC