2,593 research outputs found
Opportunistic Relaying in Wireless Networks
Relay networks having source-to-destination pairs and half-duplex
relays, all operating in the same frequency band in the presence of block
fading, are analyzed. This setup has attracted significant attention and
several relaying protocols have been reported in the literature. However, most
of the proposed solutions require either centrally coordinated scheduling or
detailed channel state information (CSI) at the transmitter side. Here, an
opportunistic relaying scheme is proposed, which alleviates these limitations.
The scheme entails a two-hop communication protocol, in which sources
communicate with destinations only through half-duplex relays. The key idea is
to schedule at each hop only a subset of nodes that can benefit from
\emph{multiuser diversity}. To select the source and destination nodes for each
hop, it requires only CSI at receivers (relays for the first hop, and
destination nodes for the second hop) and an integer-value CSI feedback to the
transmitters. For the case when is large and is fixed, it is shown that
the proposed scheme achieves a system throughput of bits/s/Hz. In
contrast, the information-theoretic upper bound of bits/s/Hz
is achievable only with more demanding CSI assumptions and cooperation between
the relays. Furthermore, it is shown that, under the condition that the product
of block duration and system bandwidth scales faster than , the
achievable throughput of the proposed scheme scales as .
Notably, this is proven to be the optimal throughput scaling even if
centralized scheduling is allowed, thus proving the optimality of the proposed
scheme in the scaling law sense.Comment: 17 pages, 8 figures, To appear in IEEE Transactions on Information
Theor
Throughput Scaling of Wireless Networks With Random Connections
This work studies the throughput scaling laws of ad hoc wireless networks in
the limit of a large number of nodes. A random connections model is assumed in
which the channel connections between the nodes are drawn independently from a
common distribution. Transmitting nodes are subject to an on-off strategy, and
receiving nodes employ conventional single-user decoding. The following results
are proven:
1) For a class of connection models with finite mean and variance, the
throughput scaling is upper-bounded by for single-hop schemes, and
for two-hop (and multihop) schemes.
2) The throughput scaling is achievable for a specific
connection model by a two-hop opportunistic relaying scheme, which employs
full, but only local channel state information (CSI) at the receivers, and
partial CSI at the transmitters.
3) By relaxing the constraints of finite mean and variance of the connection
model, linear throughput scaling is achievable with Pareto-type
fading models.Comment: 13 pages, 4 figures, To appear in IEEE Transactions on Information
Theor
Joint Scheduling and ARQ for MU-MIMO Downlink in the Presence of Inter-Cell Interference
User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the
core of high rate data-oriented downlink schemes of the next-generation of
cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users
according to their channels vector directions and SINR levels. However, when
scheduling is applied independently in each cell, the inter-cell interference
(ICI) power at each user receiver is not known in advance since it changes at
each new scheduling slot depending on the scheduling decisions of all
interfering base stations. In order to cope with this uncertainty, we consider
the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat
reQuest (ARQ). We develop a game-theoretic framework for this problem and build
on stochastic optimization techniques in order to find optimal scheduling and
ARQ schemes. Particularizing our framework to the case of "outage service
rates", we obtain a scheme based on adaptive variable-rate coding at the
physical layer, combined with ARQ at the Logical Link Control (ARQ-LLC). Then,
we present a novel scheme based on incremental redundancy Hybrid ARQ (HARQ)
that is able to achieve a throughput performance arbitrarily close to the
"genie-aided service rates", with no need for a genie that provides
non-causally the ICI power levels. The novel HARQ scheme is both easier to
implement and superior in performance with respect to the conventional
combination of adaptive variable-rate coding and ARQ-LLC.Comment: Submitted to IEEE Transactions on Communications, v2: small
correction
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Opportunistic Interference Mitigation Achieves Optimal Degrees-of-Freedom in Wireless Multi-cell Uplink Networks
We introduce an opportunistic interference mitigation (OIM) protocol, where a
user scheduling strategy is utilized in -cell uplink networks with
time-invariant channel coefficients and base stations (BSs) having
antennas. Each BS opportunistically selects a set of users who generate the
minimum interference to the other BSs. Two OIM protocols are shown according to
the number of simultaneously transmitting users per cell: opportunistic
interference nulling (OIN) and opportunistic interference alignment (OIA).
Then, their performance is analyzed in terms of degrees-of-freedom (DoFs). As
our main result, it is shown that DoFs are achievable under the OIN
protocol with selected users per cell, if the total number of users in
a cell scales at least as . Similarly, it turns out that
the OIA scheme with () selected users achieves DoFs, if scales
faster than . These results indicate that there exists a
trade-off between the achievable DoFs and the minimum required . By deriving
the corresponding upper bound on the DoFs, it is shown that the OIN scheme is
DoF optimal. Finally, numerical evaluation, a two-step scheduling method, and
the extension to multi-carrier scenarios are shown.Comment: 18 pages, 3 figures, Submitted to IEEE Transactions on Communication
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information
This paper considers utility optimal power control for energy harvesting
wireless devices with a finite capacity battery. The distribution information
of the underlying wireless environment and harvestable energy is unknown and
only outdated system state information is known at the device controller. This
scenario shares similarity with Lyapunov opportunistic optimization and online
learning but is different from both. By a novel combination of Zinkevich's
online gradient learning technique and the drift-plus-penalty technique from
Lyapunov opportunistic optimization, this paper proposes a learning-aided
algorithm that achieves utility within of the optimal, for any
desired , by using a battery with an capacity. The
proposed algorithm has low complexity and makes power investment decisions
based on system history, without requiring knowledge of the system state or its
probability distribution.Comment: This version extends v1 (our INFOCOM 2018 paper): (1) add a new
section (Section V) to study the case where utility functions are non-i.i.d.
arbitrarily varying (2) add more simulation experiments. The current version
is published in IEEE/ACM Transactions on Networkin
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