167 research outputs found
Joint User Scheduling and Beam Selection Optimization for Beam-Based Massive MIMO Downlinks
In beam-based massive multiple-input multiple-output systems, signals are
processed spatially in the radio-frequency (RF) front-end and thereby the
number of RF chains can be reduced to save hardware cost, power consumptions
and pilot overhead. Most existing work focuses on how to select, or design
analog beams to achieve performance close to full digital systems. However,
since beams are strongly correlated (directed) to certain users, the selection
of beams and scheduling of users should be jointly considered. In this paper,
we formulate the joint user scheduling and beam selection problem based on the
Lyapunov-drift optimization framework and obtain the optimal scheduling policy
in a closed-form. For reduced overhead and computational cost, the proposed
scheduling schemes are based only upon statistical channel state information.
Towards this end, asymptotic expressions of the downlink broadcast channel
capacity are derived. To address the weighted sum rate maximization problem in
the Lyapunov optimization, an algorithm based on block coordinated update is
proposed and proved to converge to the optimum of the relaxed problem. To
further reduce the complexity, an incremental greedy scheduling algorithm is
also proposed, whose performance is proved to be bounded within a constant
multiplicative factor. Simulation results based on widely-used spatial channel
models are given. It is shown that the proposed schemes are close to optimal,
and outperform several state-of-the-art schemes.Comment: Submitted to Trans. Wireless Commu
Optimal Discrete Spatial Compression for Beamspace Massive MIMO Signals
Deploying massive number of antennas at the base station side can boost the
cellular system performance dramatically. Meanwhile, it however involves
significant additional radio-frequency (RF) front-end complexity, hardware cost
and power consumption. To address this issue, the
beamspace-multiple-input-multiple-output (beamspace-MIMO) based approach is
considered as a promising solution. In this paper, we first show that the
traditional beamspace-MIMO suffers from spatial power leakage and imperfect
channel statistics estimation. A beam combination module is hence proposed,
which consists of a small number (compared with the number of antenna elements)
of low-resolution (possibly one-bit) digital (discrete) phase shifters after
the beamspace transformation to further compress the beamspace signal
dimensionality, such that the number of RF chains can be reduced beyond
beamspace transformation and beam selection. The optimum discrete beam
combination weights for the uplink are obtained based on the branch-and-bound
(BB) approach. The key to the BB-based solution is to solve the embodied
sub-problem, whose solution is derived in a closed-form. Based on the solution,
a sequential greedy beam combination scheme with linear-complexity (w.r.t. the
number of beams in the beamspace) is proposed. Link-level simulation results
based on realistic channel models and long-term-evolution (LTE) parameters are
presented which show that the proposed schemes can reduce the number of RF
chains by up to with a one-bit digital phase-shifter-network.Comment: Submitted to IEEE Trans. Signal Proces
Task Replication for Deadline-Constrained Vehicular Cloud Computing: Optimal Policy, Performance Analysis and Implications on Road Traffic
In vehicular cloud computing (VCC) systems, the computational resources of
moving vehicles are exploited and managed by infrastructures, e.g., roadside
units, to provide computational services. The offloading of computational tasks
and collection of results rely on successful transmissions between vehicles and
infrastructures during encounters. In this paper, we investigate how to provide
timely computational services in VCC systems. In particular, we seek to
minimize the deadline violation probability given a set of tasks to be executed
in vehicular clouds. Due to the uncertainty of vehicle movements, the task
replication methodology is leveraged which allows one task to be executed by
several vehicles, and thus trading computational resources for delay reduction.
The optimal task replication policy is of key interest. We first formulate the
problem as a finite-horizon sampled-time Markov decision problem and obtain the
optimal policy by value iterations. To conquer the complexity issue, we propose
the balanced-task-assignment (BETA) policy which is proved optimal and has a
clear structure: it always assigns the task with the minimum number of
replicas. Moreover, a tight closed-form performance upper bound for the BETA
policy is derived, which indicates that the deadline violation probability
follows the Rayleigh distribution approximately. Applying the vehicle
speed-density relationship in the traffic flow theory, we find that vehicle
mobility benefits VCC systems more compared with road traffic systems, by
showing that the optimum vehicle speed to minimize the deadline violation
probability is larger than the critical vehicle speed in traffic theory which
maximizes traffic flow efficiency.Comment: accepted by Internet of Things Journa
Can Decentralized Status Update Achieve Universally Near-Optimal Age-of-Information in Wireless Multiaccess Channels?
In an Internet-of-Things system where status data are collected from sensors
and actuators for time-critical applications, the freshness of data is vital
and can be quantified by the recently proposed age-of-information (AoI) metric.
In this paper, we first consider a general scenario where multiple terminals
share a common channel to transmit or receive randomly generated status
packets. The optimal scheduling problem to minimize AoI is formulated as a
restless multi-armed bandit problem. To solve the problem efficiently, we
derive the Whittle's index in closed-form and establish the indexability
thereof. Compared with existing work, we extend the index policy for AoI
optimization to incorporate stochastic packet arrivals and optimal packet
management (buffering the latest packet). Inspired by the index policy which
has near-optimal performance but is centralized by nature, a decentralized
status update scheme, i.e., the index-prioritized random access policy (IPRA),
is further proposed, achieving universally near-optimal AoI performance and
outperforming state-of-the-arts in the literature.Comment: Submitted to ITC 30, 201
Optimal Sleeping Mechanism for Multiple Servers with MMPP-Based Bursty Traffic Arrival
An important fundamental problem in green communications and networking is
the operation of servers (routers or base stations) with sleeping mechanism to
optimize energy-delay tradeoffs. This problem is very challenging when
considering realistic bursty, non-Poisson traffic. We prove for the first time
the optimal structure of such a sleep mechanism for multiple servers when the
arrival of jobs is modeled by a bursty Markov-modulated Poisson process (MMPP).
It is shown that the optimal operation, which determines the number of active
(or sleeping) servers dynamically, is hysteretic and monotone, and hence it is
a queue-threshold-based policy. This work settles a conjecture in the
literature that the optimal sleeping mechanism for a single server with
interrupted Poisson arrival process, which can be treated as a special case of
MMPP, is queue-threshold-based. The exact thresholds are given by numerically
solving the Markov decision process.Comment: Submitted to IEEE WC
Exploiting Moving Intelligence: Delay-Optimized Computation Offloading in Vehicular Fog Networks
Future vehicles will have rich computing resources to support autonomous
driving and be connected by wireless technologies. Vehicular fog networks
(VeFN) have thus emerged to enable computing resource sharing via computation
task offloading, providing wide range of fog applications. However, the high
mobility of vehicles makes it hard to guarantee the delay that accounts for
both communication and computation throughout the whole task offloading
procedure. In this article, we first review the state-of-the-art of task
offloading in VeFN, and argue that mobility is not only an obstacle for timely
computing in VeFN, but can also benefit the delay performance. We then identify
machine learning and coded computing as key enabling technologies to address
and exploit mobility in VeFN. Case studies are provided to illustrate how to
adapt learning algorithms to fit for the dynamic environment in VeFN, and how
to exploit the mobility with opportunistic computation offloading and task
replication.Comment: 7 pages, 4 figures, accepted by IEEE Communications Magazin
How Often Should CSI be Updated for Massive MIMO Systems with Massive Connectivity?
Massive multiple-input multiple-output (MIMO) systems need to support massive
connectivity for the application of the Internet of things (IoT). The overhead
of channel state information (CSI) acquisition becomes a bottleneck in the
system performance due to the increasing number of users. An intermittent
estimation scheme is proposed to ease the burden of channel estimation and
maximize the sum capacity. In the scheme, we exploit the temporal correlation
of MIMO channels and analyze the influence of the age of CSI on the downlink
transmission rate using linear precoders. We show the CSI updating interval
should follow a quasi-periodic distribution and reach a trade-off between the
accuracy of CSI estimation and the overhead of CSI acquisition by optimizing
the CSI updating frequency of each user. Numerical results show that the
proposed intermittent scheme provides significant capacity gains over the
conventional continuous estimation scheme.Comment: To appear in GLOBECOM 201
Inferring Remote Channel State Information: Cram\'er-Rao Lower Bound and Deep Learning Implementation
Channel state information (CSI) is of vital importance in wireless
communication systems. Existing CSI acquisition methods usually rely on pilot
transmissions, and geographically separated base stations (BSs) with
non-correlated CSI need to be assigned with orthogonal pilots which occupy
excessive system resources. Our previous work adopts a data-driven deep
learning based approach which leverages the CSI at a local BS to infer the CSI
remotely, however the relevance of CSI between separated BSs is not specified
explicitly. In this paper, we exploit a model-based methodology to derive the
Cram\'er-Rao lower bound (CRLB) of remote CSI inference given the local CSI.
Although the model is simplified, the derived CRLB explicitly illustrates the
relationship between the inference performance and several key system
parameters, e.g., terminal distance and antenna array size. In particular, it
shows that by leveraging multiple local BSs, the inference error exhibits a
larger power-law decay rate (w.r.t. number of antennas), compared with a single
local BS; this explains and validates our findings in evaluating the
deep-neural-network-based (DNN-based) CSI inference. We further improve on the
DNN-based method by employing dropout and deeper networks, and show an
inference performance of approximately accuracy in a realistic scenario
with CSI generated by a ray-tracing simulator.Comment: To appear at GLOBECOM 201
Remote Channel Inference for Beamforming in Ultra-Dense Hyper-Cellular Network
In this paper, we propose a learning-based low-overhead channel estimation
method for coordinated beamforming in ultra-dense networks. We first show
through simulation that the channel state information (CSI) of geographically
separated base stations (BSs) exhibits strong non-linear correlations in terms
of mutual information. This finding enables us to adopt a novel learning-based
approach to remotely infer the quality of different beamforming patterns at a
dense-layer BS based on the CSI of an umbrella control-layer BS. The proposed
scheme can reduce channel acquisition overhead by replacing pilot-aided channel
estimation with the online inference from an artificial neural network, which
is fitted offline. Moreover, we propose to exploit joint learning of multiple
CBSs and involve more candidate beam patterns to obtain better performance.
Simulation results based on stochastic ray-tracing channel models show that the
proposed scheme can reach an accuracy of 99.74% in settings with 20 beamforming
patterns.Comment: Submitted to Globecom 201
Status from a Random Field: How Densely Should One Update?
In many applications, status information of a general spatial process, in
contrast to a point information source, is of interest. In this paper, we
consider a system where status information is drawn from a random field and
transmitted to a fusion center through a wireless multiaccess channel. The
optimal density of spatial sampling points to minimize the remote status
estimation error is investigated. Assuming a one-dimensional Gauss Markov
random field and an exponential correlation function, closed-form expressions
of remote estimation error are obtained for First-Come First-Served (FCFS) and
Last-Come First-Served (LCFS) service disciplines. The optimal spatial sampling
density for the LCFS case is given explicitly. Simulation results are presented
which agree with our analysis
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