13,106 research outputs found
Adaptive channel selection for DOA estimation in MIMO radar
We present adaptive strategies for antenna selection for Direction of Arrival
(DoA) estimation of a far-field source using TDM MIMO radar with linear arrays.
Our treatment is formulated within a general adaptive sensing framework that
uses one-step ahead predictions of the Bayesian MSE using a parametric family
of Weiss-Weinstein bounds that depend on previous measurements. We compare in
simulations our strategy with adaptive policies that optimize the Bobrovsky-
Zaka{\i} bound and the Expected Cram\'er-Rao bound, and show the performance
for different levels of measurement noise.Comment: Submitted to the 25th European Signal Processing Conference
(EUSIPCO), 201
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
An Efficient Polyphase Filter Based Resampling Method for Unifying the PRFs in SAR Data
Variable and higher pulse repetition frequencies (PRFs) are increasingly
being used to meet the stricter requirements and complexities of current
airborne and spaceborne synthetic aperture radar (SAR) systems associated with
higher resolution and wider area products. POLYPHASE, the proposed resampling
scheme, downsamples and unifies variable PRFs within a single look complex
(SLC) SAR acquisition and across a repeat pass sequence of acquisitions down to
an effective lower PRF. A sparsity condition of the received SAR data ensures
that the uniformly resampled data approximates the spectral properties of a
decimated densely sampled version of the received SAR data. While experiments
conducted with both synthetically generated and real airborne SAR data show
that POLYPHASE retains comparable performance to the state-of-the-art BLUI
scheme in image quality, a polyphase filter-based implementation of POLYPHASE
offers significant computational savings for arbitrary (not necessarily
periodic) input PRF variations, thus allowing fully on-board, in-place, and
real-time implementation
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