The paper considers the problem of distributed adaptive linear parameter
estimation in multi-agent inference networks. Local sensing model information
is only partially available at the agents and inter-agent communication is
assumed to be unpredictable. The paper develops a generic mixed time-scale
stochastic procedure consisting of simultaneous distributed learning and
estimation, in which the agents adaptively assess their relative observation
quality over time and fuse the innovations accordingly.

A new approach is proposed, namely CSSF MIMO radar, which applies the
technique of step frequency (SF) to compressive sensing (CS) based multi-input
multi-output (MIMO) radar. The proposed approach enables high resolution range,
angle and Doppler estimation, while transmitting narrowband pulses. The problem
of joint angle-Doppler-range estimation is first formulated to fit the CS
framework, i.e., as an L1 optimization problem. Direct solution of this problem
entails high complexity as it employs a basis matrix whose construction
requires discretization of the angle-Doppler-range space.

This paper considers several linear beamformer design paradigms for multiuser
time-invariant multiple-input multiple-output interference channels. Notably,
interference alignment and sum-rate based algorithms such as the maximum
signal-to-interference-plus noise (max-SINR) algorithm are considered.

Ensuring the usefulness of electronic data sources while providing necessary
privacy guarantees is an important unsolved problem. This problem drives the
need for an overarching analytical framework that can quantify the safety of
personally identifiable information (privacy) while still providing a
quantifable benefit (utility) to multiple legitimate information consumers.
State of the art approaches have predominantly focused on privacy.

The multiuser communication channel, in which multiple users exchange
information with the help of a relay terminal, termed the "multi-way relay
channel" (mRC), is introduced. In this model, multiple interfering clusters of
users communicate simultaneously, where the users within the same cluster wish
to exchange messages among themselves. It is assumed that the users cannot
receive each other's signals directly, and hence the relay terminal is the
enabler of communication.

The problem of private information "leakage" (inadvertently or by malicious
design) from the myriad large centralized searchable data repositories drives
the need for an analytical framework that quantifies unequivocally how safe
private data can be (privacy) while still providing useful benefit (utility) to
multiple legitimate information consumers.

This paper considers the problem of secret communication over a two-receiver
multiple-input multiple-output (MIMO) Gaussian broadcast channel. The
transmitter has two independent, confidential messages and a common message.
Each of the confidential messages is intended for one of the receivers but
needs to be kept perfectly secret from the other, and the common message is
intended for both receivers. It is shown that a natural scheme that combines
secret dirty-paper coding with Gaussian superposition coding achieves the
secrecy capacity region.

Ellipse and ellipsoid fitting has been extensively researched and widely
applied. Although traditional fitting methods provide accurate estimation of
ellipse parameters in the low-noise case, their performance is compromised when
the noise level or the ellipse eccentricity are high. A series of robust
fitting algorithms are proposed that perform well in high-noise,
high-eccentricity ellipse/spheroid (a special class of ellipsoid) cases. The
new algorithms are based on the geometric definition of an ellipse/spheroid,
and improved using global statistical properties of the data.

The authors recently proposed a MIMO radar system that is implemented by a
small wireless network. By applying compressive sensing (CS) at the receive
nodes, the MIMO radar super-resolution can be achieved with far fewer
observations than conventional approaches. This previous work considered the
estimation of direction of arrival and Doppler. Since the targets are sparse in
the angle-velocity space, target information can be extracted by solving an l1
minimization problem. In this paper, the range information is exploited by
introducing step frequency to MIMO radar with CS.

The capacity regions of multiple-input multiple-output Gaussian
Z-interference channels are established for the very strong interference and
aligned strong interference cases. The sum-rate capacity of such channels is
established under noisy interference. These results generalize known results
for scalar Gaussian Z-interference channels.

In this paper, the theoretical limits on the robustness of MIMO joint source
channel codes is investigated. The case in which a single joint source channel
code is used for the entire range of SNRs and for all levels of required
fidelity is considered. Limits on the asymptotic performance of such a system
are characterized in terms of upper bounds on the diversity-fidelity tradeoff,
which can be viewed as an analog version of the diversity-multiplexing
tradeoff.

In this paper, an outlier elimination algorithm for ellipse/ellipsoid fitting
is proposed. This two-stage algorithm employs a proximity-based outlier
detection algorithm (using the graph Laplacian), followed by a model-based
outlier detection algorithm similar to random sample consensus (RANSAC). These
two stages compensate for each other so that outliers of various types can be
eliminated with reasonable computation. The outlier elimination algorithm
considerably improves the robustness of ellipse/ellipsoid fitting as
demonstrated by simulations.

Sufficient conditions required to achieve the interference-free capacity
region of ergodic fading K-user interference channels (IFCs) are obtained. In
particular, this capacity region is shown to be achieved when every receiver
decodes all K transmitted messages such that the channel statistics and the
waterfilling power policies for all K (interference-free) links satisfy a set
of K(K-1) ergodic very strong conditions. The result is also of independent
interest in combinatorics.

The fading cognitive multiple-access channel with confidential messages
(MAC-CM) is investigated, in which two users attempt to transmit common
information to a destination and user 1 also has confidential information
intended for the destination. User 1 views user 2 as an eavesdropper and wishes
to keep its confidential information as secret as possible from user 2. The
multiple-access channel (both the user-to-user channel and the
user-to-destination channel) is corrupted by multiplicative fading gain
coefficients in addition to additive white Gaussian noise.

For a multi-user interference channel with multi-antenna transmitters and
single-antenna receivers, by restricting each receiver to a single-user
detector, computing the largest achievable rate region amounts to solving a
family of non-convex optimization problems. Recognizing the intrinsic
connection between the signal power at the intended receiver and the
interference power at the unintended receiver, the original family of
non-convex optimization problems is converted into a new family of convex
optimization problems.

The sum capacity of a class of layered erasure one-sided interference
channels is developed under the assumption of no channel state information at
the transmitters. Outer bounds are presented for this model and are shown to be
tight for the following sub-classes: i) weak, ii) strong (mix of strong but not
very strong (SnVS) and very strong (VS)), iii) ergodic very strong (mix of
strong and weak), and (iv) a sub-class of mixed interference (mix of SnVS and
weak). Each sub-class is uniquely defined by the fading statistics.