559 research outputs found
Capacity of Compound MIMO Gaussian Channels with Additive Uncertainty
This paper considers reliable communications over a multiple-input
multiple-output (MIMO) Gaussian channel, where the channel matrix is within a
bounded channel uncertainty region around a nominal channel matrix, i.e., an
instance of the compound MIMO Gaussian channel. We study the optimal transmit
covariance matrix design to achieve the capacity of compound MIMO Gaussian
channels, where the channel uncertainty region is characterized by the spectral
norm. This design problem is a challenging non-convex optimization problem.
However, in this paper, we reveal that this problem has a hidden convexity
property, which can be exploited to map the problem into a convex optimization
problem. We first prove that the optimal transmit design is to diagonalize the
nominal channel, and then show that the duality gap between the capacity of the
compound MIMO Gaussian channel and the min-max channel capacity is zero, which
proves the conjecture of Loyka and Charalambous (IEEE Trans. Inf. Theory, vol.
58, no. 4, pp. 2048-2063, 2012). The key tools for showing these results are a
new matrix determinant inequality and some unitarily invariant properties.Comment: 8 pages, submitted to IEEE Transactions on Information Theor
Efficient Beam Alignment in Millimeter Wave Systems Using Contextual Bandits
In this paper, we investigate the problem of beam alignment in millimeter
wave (mmWave) systems, and design an optimal algorithm to reduce the overhead.
Specifically, due to directional communications, the transmitter and receiver
beams need to be aligned, which incurs high delay overhead since without a
priori knowledge of the transmitter/receiver location, the search space spans
the entire angular domain. This is further exacerbated under dynamic conditions
(e.g., moving vehicles) where the access to the base station (access point) is
highly dynamic with intermittent on-off periods, requiring more frequent beam
alignment and signal training. To mitigate this issue, we consider an online
stochastic optimization formulation where the goal is to maximize the
directivity gain (i.e., received energy) of the beam alignment policy within a
time period. We exploit the inherent correlation and unimodality properties of
the model, and demonstrate that contextual information improves the
performance. To this end, we propose an equivalent structured Multi-Armed
Bandit model to optimally exploit the exploration-exploitation tradeoff. In
contrast to the classical MAB models, the contextual information makes the
lower bound on regret (i.e., performance loss compared with an oracle policy)
independent of the number of beams. This is a crucial property since the number
of all combinations of beam patterns can be large in transceiver antenna
arrays, especially in massive MIMO systems. We further provide an
asymptotically optimal beam alignment algorithm, and investigate its
performance via simulations.Comment: To Appear in IEEE INFOCOM 2018. arXiv admin note: text overlap with
arXiv:1611.05724 by other author
Scheduling of Multicast and Unicast Services under Limited Feedback by using Rateless Codes
Many opportunistic scheduling techniques are impractical because they require
accurate channel state information (CSI) at the transmitter. In this paper, we
investigate the scheduling of unicast and multicast services in a downlink
network with a very limited amount of feedback information. Specifically,
unicast users send imperfect (or no) CSI and infrequent acknowledgements (ACKs)
to a base station, and multicast users only report infrequent ACKs to avoid
feedback implosion. We consider the use of physical-layer rateless codes, which
not only combats channel uncertainty, but also reduces the overhead of ACK
feedback. A joint scheduling and power allocation scheme is developed to
realize multiuser diversity gain for unicast service and multicast gain for
multicast service. We prove that our scheme achieves a near-optimal throughput
region. Our simulation results show that our scheme significantly improves the
network throughput over schemes employing fixed-rate codes or using only
unicast communications
Update or Wait: How to Keep Your Data Fresh
In this work, we study how to optimally manage the freshness of information
updates sent from a source node to a destination via a channel. A proper metric
for data freshness at the destination is the age-of-information, or simply age,
which is defined as how old the freshest received update is since the moment
that this update was generated at the source node (e.g., a sensor). A
reasonable update policy is the zero-wait policy, i.e., the source node submits
a fresh update once the previous update is delivered and the channel becomes
free, which achieves the maximum throughput and the minimum delay.
Surprisingly, this zero-wait policy does not always minimize the age. This
counter-intuitive phenomenon motivates us to study how to optimally control
information updates to keep the data fresh and to understand when the zero-wait
policy is optimal. We introduce a general age penalty function to characterize
the level of dissatisfaction on data staleness and formulate the average age
penalty minimization problem as a constrained semi-Markov decision problem
(SMDP) with an uncountable state space. We develop efficient algorithms to find
the optimal update policy among all causal policies, and establish sufficient
and necessary conditions for the optimality of the zero-wait policy. Our
investigation shows that the zero-wait policy is far from the optimum if (i)
the age penalty function grows quickly with respect to the age, (ii) the packet
transmission times over the channel are positively correlated over time, or
(iii) the packet transmission times are highly random (e.g., following a
heavy-tail distribution)
Deploy-DDS: Tool framework for supporting deployment architecture of data distribution service based systems
Data Distribution Service (DDS) is the Object Management Group's (OMG) new standard middleware after Common Object Request Broker Architecture (CORBA), which is becoming increasingly popular. One of the important problems in DDS Based Software Systems is the deployment configuration of DDS modules to the physical resources. In general, this can be done in many different ways whereby each deployment alternative will perform differently. Currently, the deployment configuration is decided after the coding phase and usually performed manually. For large configurations, finding the feasible deployment might require serious rework with costly and time consuming iterations. In this paper, we present the tool Deploy-DDS to support the selection and generation of deployment architectures of DDS based systems. The tool can be used to perform an evaluation during the design phase and generate the selected feasible configuration. © 2014 Authors
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