109,723 research outputs found
Minimum Length Scheduling for Discrete-Rate Full-Duplex Wireless Powered Communication Networks
In this paper, we consider a wireless powered communication network where
multiple users with RF energy harvesting capabilities communicate to a hybrid
energy and information access point (HAP) in full-duplex mode. Each user has to
transmit a certain amount of data with a transmission rate from a finite set of
discrete rate levels, using the energy initially available in its battery and
the energy it can harvest until the end of its transmission. Considering this
model, we propose a novel discrete rate based minimum length scheduling problem
to determine the optimal power control, rate adaptation and transmission
schedule subject to data, energy causality and maximum transmit power
constraints. The proposed optimization problem is proven to be NP-hard which
requires exponential-time algorithms to solve for the global optimum. As a
solution strategy, first, we demonstrate that the power control and rate
adaptation, and scheduling problems can be solved separately in the optimal
solution. For the power control and rate adaptation problem, we derive the
optimal solution based on the proposed minimum length scheduling slot
definition. For the scheduling, we classify the problem based on the
distribution of minimum length scheduling slots of the users over time. For the
non-overlapping slots scenario, we present the optimal scheduling algorithm.
For the overlapping scenario, we propose a polynomial-time heuristic scheduling
algorithm
Joint Base Station Clustering and Beamformer Design for Partial Coordinated Transmission in Heterogenous Networks
We consider the interference management problem in a multicell MIMO
heterogenous network. Within each cell there are a large number of distributed
micro/pico base stations (BSs) that can be potentially coordinated for joint
transmission. To reduce coordination overhead, we consider user-centric BS
clustering so that each user is served by only a small number of (potentially
overlapping) BSs. Thus, given the channel state information, our objective is
to jointly design the BS clustering and the linear beamformers for all BSs in
the network. In this paper, we formulate this problem from a {sparse
optimization} perspective, and propose an efficient algorithm that is based on
iteratively solving a sequence of group LASSO problems. A novel feature of the
proposed algorithm is that it performs BS clustering and beamformer design
jointly rather than separately as is done in the existing approaches for
partial coordinated transmission. Moreover, the cluster size can be controlled
by adjusting a single penalty parameter in the nonsmooth regularized utility
function. The convergence of the proposed algorithm (to a local optimal
solution) is guaranteed, and its effectiveness is demonstrated via extensive
simulation.Comment: Accepted by IEEE Journal on Selected Areas in Communications, special
issues on Large-Scale multiple-antenna system
Symbiotic Radio: A New Communication Paradigm for Passive Internet-of-Things
In this paper, a novel technique, called symbiotic radio (SR), is proposed
for passive Internet-of-Things (IoT), in which a backscatter device (BD) is
integrated with a primary transmission. The primary transmitter is designed to
assist the primary and BD transmissions, and the primary receiver decodes the
information from the primary transmitter as well as the BD. We consider a
multiple-input single-output (MISO) SR and the symbol period for BD
transmission is designed to be either the same as or much longer than that of
the primary system, resulting in parasitic or commensal relationship between
the primary and BD transmissions. We first derive the achievable rates for the
primary system and the BD transmission. Then, we formulate two transmit
beamforming optimization problems, i.e., the weighted sum-rate maximization
problem and the transmit power minimization problem, and solve these non-convex
problems by applying semi-definite relaxation technique. In addition, a novel
transmit beamforming structure is proposed to reduce the computational
complexity of the solutions. Simulation results show that when the BD
transmission rate is properly designed, the proposed SR not only enables the
opportunistic transmission for the BD via energy-efficient passive
backscattering, but also enhances the achievable rate of the primary system by
properly exploiting the additional signal path from the BD
Spectrum optimization in multi-user multi-carrier systems with iterative convex and nonconvex approximation methods
Several practical multi-user multi-carrier communication systems are
characterized by a multi-carrier interference channel system model where the
interference is treated as noise. For these systems, spectrum optimization is a
promising means to mitigate interference. This however corresponds to a
challenging nonconvex optimization problem. Existing iterative convex
approximation (ICA) methods consist in solving a series of improving convex
approximations and are typically implemented in a per-user iterative approach.
However they do not take this typical iterative implementation into account in
their design. This paper proposes a novel class of iterative approximation
methods that focuses explicitly on the per-user iterative implementation, which
allows to relax the problem significantly, dropping joint convexity and even
convexity requirements for the approximations. A systematic design framework is
proposed to construct instances of this novel class, where several new
iterative approximation methods are developed with improved per-user convex and
nonconvex approximations that are both tighter and simpler to solve (in
closed-form). As a result, these novel methods display a much faster
convergence speed and require a significantly lower computational cost.
Furthermore, a majority of the proposed methods can tackle the issue of getting
stuck in bad locally optimal solutions, and hence improve solution quality
compared to existing ICA methods.Comment: 33 pages, 7 figures. This work has been submitted for possible
publicatio
Quality of Information Maximization for Wireless Networks via a Fully Separable Quadratic Policy
An information collection problem in a wireless network with random events is
considered. Wireless devices report on each event using one of multiple
reporting formats. Each format has a different quality and uses different data
lengths. Delivering all data in the highest quality format can overload system
resources. The goal is to make intelligent format selection and routing
decisions to maximize time-averaged information quality subject to network
stability. Lyapunov optimization theory can be used to solve such a problem by
repeatedly minimizing the linear terms of a quadratic drift-plus-penalty
expression. To reduce delays, this paper proposes a novel extension of this
technique that preserves the quadratic nature of the drift minimization while
maintaining a fully separable structure. In addition, to avoid high queuing
delay, paths are restricted to at most two hops. The resulting algorithm can
push average information quality arbitrarily close to optimum, with a trade-off
in queue backlog. The algorithm compares favorably to the basic
drift-plus-penalty scheme in terms of backlog and delay. Furthermore, the
technique is generalized to solve linear programs and yields smoother results
than the standard drift-plus-penalty scheme
Delivery Time Reduction for Order-Constrained Applications using Binary Network Codes
Consider a radio access network wherein a base-station is required to deliver
a set of order-constrained messages to a set of users over independent erasure
channels. This paper studies the delivery time reduction problem using
instantly decodable network coding (IDNC). Motivated by time-critical and
order-constrained applications, the delivery time is defined, at each
transmission, as the number of undelivered messages. The delivery time
minimization problem being computationally intractable, most of the existing
literature on IDNC propose sub-optimal online solutions. This paper suggests a
novel method for solving the problem by introducing the delivery delay as a
measure of distance to optimality. An expression characterizing the delivery
time using the delivery delay is derived, allowing the approximation of the
delivery time minimization problem by an optimization problem involving the
delivery delay. The problem is, then, formulated as a maximum weight clique
selection problem over the IDNC graph wherein the weight of each vertex
reflects its corresponding user and message's delay. Simulation results suggest
that the proposed solution achieves lower delivery and completion times as
compared to the best-known heuristics for delivery time reduction
Coding based Data Broadcasting for Time Critical Applications with Rate Adaptation
In this paper, we dynamically select the transmission rate and design
wireless network coding to improve the quality of services such as delay for
time critical applications. In a network coded system, with low transmission
rate and hence longer transmission range, more packets may be encoded, which
increases the coding opportunity. However, low transmission rate may incur
extra transmission delay, which is intolerable for time critical applications.
We design a novel joint rate selection and wireless network coding (RSNC)
scheme with delay constraint, so as to maximize the total benefit (where we can
define the benefit based on the priority or importance of a packet for example)
of the packets that are successfully received at the destinations without
missing their deadlines. We prove that the proposed problem is NP-hard, and
propose a novel graph model to mathematically formulate the problem. For the
general case, we propose a transmission metric and design an efficient
algorithm to determine the transmission rate and coding strategy for each
transmission. For a special case when all delay constraints are the same, we
study the pairwise coding and present a polynomial time pairwise coding
algorithm that achieves an approximation ratio of 1 - 1/e to the optimal
pairwise coding solution, where e is the base of the natural logarithm.
Finally, simulation results demonstrate the superiority of the proposed RSNC
scheme.Comment: IEEE Transactions on Vehicular Technolog
Delay-aware data transmission of multi-carrier communications in the presence of renewable energy
In the paper, we investigate the delay-aware data transmission in renewable
energy aided multi-carrier system. Besides utilizing the local renewables, the
transmitter can also purchase grid power. By scheduling the amount of
transmitted data (The data are stored in a buffer before transmission), the
sub-carrier allocation, and the renewable allocation in each transmission
period, the transmitter aims to minimize the purchasing cost under a buffer
delay constraint. By theoretical analysis of the formulated stochastic
optimization problem, we find that transmit the scheduled data through the
subcarrier with best condition is optimal and greedy renewable energy is
approximately optimal. Furthermore, based on the theoretical derives and
Lyapunov optimization, an on-line algorithm, which does NOT require future
information, is proposed. Numerical results illustrate the delay and cost
performance of the proposed algorithm. In addition, the comparisons with the
delay-optimal policy and cost-optimal policy are carried out
On the Packet Decoding Delay of Linear Network Coded Wireless Broadcast
We apply linear network coding (LNC) to broadcast a block of data packets
from one sender to a set of receivers via lossy wireless channels, assuming
each receiver already possesses a subset of these packets and wants the rest.
We aim to characterize the average packet decoding delay (APDD), which reflects
how soon each individual data packet can be decoded by each receiver on
average, and to minimize it while achieving optimal throughput. To this end, we
first derive closed-form lower bounds on the expected APDD of all LNC
techniques under random packet erasures. We then prove that these bounds are
NP-hard to achieve and, thus, that APDD minimization is an NP-hard problem. We
then study the performance of some existing LNC techniques, including random
linear network coding (RLNC) and instantly decodable network coding (IDNC). We
proved that all throughput-optimal LNC techniques can approximate the minimum
expected APDD with a ratio between 4/3 and 2. In particular, the ratio of RLNC
is exactly 2. We then prove that all IDNC techniques are only heuristics in
terms of throughput optimization and {cannot guarantee an APDD approximation
ratio for at least a subset of the receivers}. Finally, we propose
hyper-graphic linear network coding (HLNC), a novel throughput-optimal and
APDD-approximating LNC technique based on a hypergraph model of receivers'
packet reception state. We implement it under different availability of
receiver feedback, and numerically compare its performance with RLNC and a
heuristic general IDNC technique. The results show that the APDD performance of
HLNC is better under all tested system settings, even if receiver feedback is
only collected intermittently
Sleeping Multi-Armed Bandit Learning for Fast Uplink Grant Allocation in Machine Type Communications
Scheduling fast uplink grant transmissions for machine type communications
(MTCs) is one of the main challenges of future wireless systems. In this paper,
a novel fast uplink grant scheduling method based on the theory of multi-armed
bandits (MABs) is proposed. First, a single quality-of-service metric is
defined as a combination of the value of data packets, maximum tolerable access
delay, and data rate. Since full knowledge of these metrics for all machine
type devices (MTDs) cannot be known in advance at the base station (BS) and the
set of active MTDs changes over time, the problem is modeled as a sleeping MAB
with stochastic availability and a stochastic reward function. In particular,
given that, at each time step, the knowledge on the set of active MTDs is
probabilistic, a novel probabilistic sleeping MAB algorithm is proposed to
maximize the defined metric. Analysis of the regret is presented and the effect
of the prediction error of the source traffic prediction algorithm on the
performance of the proposed sleeping MAB algorithm is investigated. Moreover,
to enable fast uplink allocation for multiple MTDs at each time, a novel method
is proposed based on the concept of best arms ordering in the MAB setting.
Simulation results show that the proposed framework yields a three-fold
reduction in latency compared to a random scheduling policy since it
prioritises the scheduling of MTDs that have stricter latency requirements.
Moreover, by properly balancing the exploration versus exploitation tradeoff,
the proposed algorithm can provide system fairness by allowing the most
important MTDs to be scheduled more often while also allowing the less
important MTDs to be selected enough times to ensure the accuracy of estimation
of their importance
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