2,608 research outputs found
Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition
This paper studies joint beamforming and power control in a coordinated
multicell downlink system that serves multiple users per cell to maximize the
minimum weighted signal-to-interference-plus-noise ratio. The optimal solution
and distributed algorithm with geometrically fast convergence rate are derived
by employing the nonlinear Perron-Frobenius theory and the multicell network
duality. The iterative algorithm, though operating in a distributed manner,
still requires instantaneous power update within the coordinated cluster
through the backhaul. The backhaul information exchange and message passing may
become prohibitive with increasing number of transmit antennas and increasing
number of users. In order to derive asymptotically optimal solution, random
matrix theory is leveraged to design a distributed algorithm that only requires
statistical information. The advantage of our approach is that there is no
instantaneous power update through backhaul. Moreover, by using nonlinear
Perron-Frobenius theory and random matrix theory, an effective primal network
and an effective dual network are proposed to characterize and interpret the
asymptotic solution.Comment: Some typos in the version publised in the IEEE Transactions on
Wireless Communications are correcte
Power Aware Wireless File Downloading: A Constrained Restless Bandit Approach
This paper treats power-aware throughput maximization in a multi-user file
downloading system. Each user can receive a new file only after its previous
file is finished. The file state processes for each user act as coupled Markov
chains that form a generalized restless bandit system. First, an optimal
algorithm is derived for the case of one user. The algorithm maximizes
throughput subject to an average power constraint. Next, the one-user algorithm
is extended to a low complexity heuristic for the multi-user problem. The
heuristic uses a simple online index policy and its effectiveness is shown via
simulation. For simple 3-user cases where the optimal solution can be computed
offline, the heuristic is shown to be near-optimal for a wide range of
parameters
Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks
We consider a small extent sensor network for event detection, in which nodes
take samples periodically and then contend over a {\em random access network}
to transmit their measurement packets to the fusion center. We consider two
procedures at the fusion center to process the measurements. The Bayesian
setting is assumed; i.e., the fusion center has a prior distribution on the
change time. In the first procedure, the decision algorithm at the fusion
center is \emph{network-oblivious} and makes a decision only when a complete
vector of measurements taken at a sampling instant is available. In the second
procedure, the decision algorithm at the fusion center is \emph{network-aware}
and processes measurements as they arrive, but in a time causal order. In this
case, the decision statistic depends on the network delays as well, whereas in
the network-oblivious case, the decision statistic does not depend on the
network delays. This yields a Bayesian change detection problem with a tradeoff
between the random network delay and the decision delay; a higher sampling rate
reduces the decision delay but increases the random access delay. Under
periodic sampling, in the network--oblivious case, the structure of the optimal
stopping rule is the same as that without the network, and the optimal change
detection delay decouples into the network delay and the optimal decision delay
without the network. In the network--aware case, the optimal stopping problem
is analysed as a partially observable Markov decision process, in which the
states of the queues and delays in the network need to be maintained. A
sufficient statistic for decision is found to be the network-state and the
posterior probability of change having occurred given the measurements received
and the state of the network. The optimal regimes are studied using simulation.Comment: To appear in ACM Transactions on Sensor Networks. A part of this work
was presented in IEEE SECON 2006, and Allerton 201
Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges
With the rapid development of marine activities, there has been an increasing
number of maritime mobile terminals, as well as a growing demand for high-speed
and ultra-reliable maritime communications to keep them connected.
Traditionally, the maritime Internet of Things (IoT) is enabled by maritime
satellites. However, satellites are seriously restricted by their high latency
and relatively low data rate. As an alternative, shore & island-based base
stations (BSs) can be built to extend the coverage of terrestrial networks
using fourth-generation (4G), fifth-generation (5G), and beyond 5G services.
Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs.
Despite of all these approaches, there are still open issues for an efficient
maritime communication network (MCN). For example, due to the complicated
electromagnetic propagation environment, the limited geometrically available BS
sites, and rigorous service demands from mission-critical applications,
conventional communication and networking theories and methods should be
tailored for maritime scenarios. Towards this end, we provide a survey on the
demand for maritime communications, the state-of-the-art MCNs, and key
technologies for enhancing transmission efficiency, extending network coverage,
and provisioning maritime-specific services. Future challenges in developing an
environment-aware, service-driven, and integrated satellite-air-ground MCN to
be smart enough to utilize external auxiliary information, e.g., sea state and
atmosphere conditions, are also discussed
Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors
In this paper, we design an optimal sensor collaboration strategy among
neighboring nodes while tracking a time-varying parameter using wireless sensor
networks in the presence of imperfect communication channels. The sensor
network is assumed to be self-powered, where sensors are equipped with energy
harvesters that replenish energy from the environment. In order to minimize the
mean square estimation error of parameter tracking, we propose an online sensor
collaboration policy subject to real-time energy harvesting constraints. The
proposed energy allocation strategy is computationally light and only relies on
the second-order statistics of the system parameters. For this, we first
consider an offline non-convex optimization problem, which is solved exactly
using semidefinite programming. Based on the offline solution, we design an
online power allocation policy that requires minimal online computation and
satisfies the dynamics of energy flow at each sensor. We prove that the
proposed online policy is asymptotically equivalent to the optimal offline
solution and show its convergence rate and robustness. We empirically show that
the estimation performance of the proposed online scheme is better than that of
the online scheme when channel state information about the dynamical system is
available in the low SNR regime. Numerical results are conducted to demonstrate
the effectiveness of our approach
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