6,337 research outputs found
Throughput Optimal Decentralized Scheduling of Multi-Hop Networks with End-to-End Deadline Constraints: II Wireless Networks with Interference
Consider a multihop wireless network serving multiple flows in which wireless
link interference constraints are described by a link interference graph. For
such a network, we design routing-scheduling policies that maximize the
end-to-end timely throughput of the network. Timely throughput of a flow is
defined as the average rate at which packets of flow reach their
destination node within their deadline.
Our policy has several surprising characteristics. Firstly, we show that the
optimal routing-scheduling decision for an individual packet that is present at
a wireless node is solely a function of its location, and "age". Thus,
a wireless node does not require the knowledge of the "global" network
state in order to maximize the timely throughput. We notice that in comparison,
under the backpressure routing policy, a node requires only the knowledge
of its neighbours queue lengths in order to guarantee maximal stability, and
hence is decentralized. The key difference arises due to the fact that in our
set-up the packets loose their utility once their "age" has crossed their
deadline, thus making the task of optimizing timely throughput much more
challenging than that of ensuring network stability. Of course, due to this key
difference, the decision process involved in maximizing the timely throughput
is also much more complex than that involved in ensuring network-wide queue
stabilization. In view of this, our results are somewhat surprising
Wireless Power Transfer and Data Collection in Wireless Sensor Networks
In a rechargeable wireless sensor network, the data packets are generated by
sensor nodes at a specific data rate, and transmitted to a base station.
Moreover, the base station transfers power to the nodes by using Wireless Power
Transfer (WPT) to extend their battery life. However, inadequately scheduling
WPT and data collection causes some of the nodes to drain their battery and
have their data buffer overflow, while the other nodes waste their harvested
energy, which is more than they need to transmit their packets. In this paper,
we investigate a novel optimal scheduling strategy, called EHMDP, aiming to
minimize data packet loss from a network of sensor nodes in terms of the nodes'
energy consumption and data queue state information. The scheduling problem is
first formulated by a centralized MDP model, assuming that the complete states
of each node are well known by the base station. This presents the upper bound
of the data that can be collected in a rechargeable wireless sensor network.
Next, we relax the assumption of the availability of full state information so
that the data transmission and WPT can be semi-decentralized. The simulation
results show that, in terms of network throughput and packet loss rate, the
proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular
Technolog
Throughput Optimal Decentralized Scheduling of Multi-Hop Networks with End-to-End Deadline Constraints: Unreliable Links
We consider unreliable multi-hop networks serving multiple flows in which
packets not delivered to their destination nodes by their deadlines are
dropped. We address the design of policies for routing and scheduling packets
that optimize any specified weighted average of the throughputs of the flows.
We provide a new approach which directly yields an optimal distributed
scheduling policy that attains any desired maximal timely-throughput vector
under average-power constraints on the nodes. It pursues a novel intrinsically
stochastic decomposition of the Lagrangian of the constrained network-wide MDP
rather than of the fluid model. All decisions regarding a packet's transmission
scheduling, transmit power level, and routing, are completely distributed,
based solely on the age of the packet, not requiring any knowledge of network
state or queue lengths at any of the nodes. Global coordination is achieved
through a tractably computable "price" for transmission energy. This price is
different from that used to derive the backpressure policy where price
corresponds to queue lengths. A quantifiably near-optimal policy is provided if
nodes have peak-power constraints
Distributed Rate Allocation for Wireless Networks
This paper develops a distributed algorithm for rate allocation in wireless
networks that achieves the same throughput region as optimal centralized
algorithms. This cross-layer algorithm jointly performs medium access control
(MAC) and physical-layer rate adaptation. The paper establishes that this
algorithm is throughput-optimal for general rate regions. In contrast to on-off
scheduling, rate allocation enables optimal utilization of physical-layer
schemes by scheduling multiple rate levels. The algorithm is based on local
queue-length information, and thus the algorithm is of significant practical
value. The algorithm requires that each link can determine the global
feasibility of increasing its current data-rate. In many classes of networks,
any one link's data-rate primarily impacts its neighbors and this impact decays
with distance. Hence, local exchanges can provide the information needed to
determine feasibility. Along these lines, the paper discusses the potential use
of existing physical-layer control messages to determine feasibility. This can
be considered as a technique analogous to carrier sensing in CSMA (Carrier
Sense Multiple Access) networks. An important application of this algorithm is
in multiple-band multiple-radio throughput-optimal distributed scheduling for
white-space networks.Comment: 39 pages, 4 figure
Decentralized Traffic Management Strategies for Sensor-Enabled Cars
Traffic Congestions and accidents are major concerns in today's
transportation systems. This thesis investigates how to optimize traffic flow
on highways, in particular for merging situations such as intersections where a
ramp leads onto the highway. In our work, cars are equipped with sensors that
can detect distance to neighboring cars, and communicate their velocity and
acceleration readings with one another. Sensor-enabled cars can locally
exchange sensed information about the traffic and adapt their behavior much
earlier than regular cars.
We propose proactive algorithms for merging different streams of
sensor-enabled cars into a single stream. A proactive merging algorithm
decouples the decision point from the actual merging point. Sensor-enabled cars
allow us to decide where and when a car merges before it arrives at the actual
merging point. This leads to a significant improvement in traffic flow as
velocities can be adjusted appropriately. We compare proactive merging
algorithms against the conventional priority-based merging algorithm in a
controlled simulation environment. Experiment results show that proactive
merging algorithms outperform the priority-based merging algorithm in terms of
flow and delay
Optimality of Treating Interference as Noise: A Combinatorial Perspective
For single-antenna Gaussian interference channels, we re-formulate the
problem of determining the Generalized Degrees of Freedom (GDoF) region
achievable by treating interference as Gaussian noise (TIN) derived in [3] from
a combinatorial perspective. We show that the TIN power control problem can be
cast into an assignment problem, such that the globally optimal power
allocation variables can be obtained by well-known polynomial time algorithms.
Furthermore, the expression of the TIN-Achievable GDoF region (TINA region) can
be substantially simplified with the aid of maximum weighted matchings. We also
provide conditions under which the TINA region is a convex polytope that relax
those in [3]. For these new conditions, together with a channel connectivity
(i.e., interference topology) condition, we show TIN optimality for a new class
of interference networks that is not included, nor includes, the class found in
[3].
Building on the above insights, we consider the problem of joint link
scheduling and power control in wireless networks, which has been widely
studied as a basic physical layer mechanism for device-to-device (D2D)
communications. Inspired by the relaxed TIN channel strength condition as well
as the assignment-based power allocation, we propose a low-complexity
GDoF-based distributed link scheduling and power control mechanism (ITLinQ+)
that improves upon the ITLinQ scheme proposed in [4] and further improves over
the heuristic approach known as FlashLinQ. It is demonstrated by simulation
that ITLinQ+ provides significant average network throughput gains over both
ITLinQ and FlashLinQ, and yet still maintains the same level of implementation
complexity. More notably, the energy efficiency of the newly proposed ITLinQ+
is substantially larger than that of ITLinQ and FlashLinQ, which is desirable
for D2D networks formed by battery-powered devices.Comment: A short version has been presented at IEEE International Symposium on
Information Theory (ISIT 2015), Hong Kon
A Common Information-Based Multiple Access Protocol Achieving Full Throughput and Linear Delay
We consider a multiple access communication system where multiple users share
a common collision channel. Each user observes its local traffic and the
feedback from the channel. At each time instant the feedback from the channel
is one of three messages: no transmission, successful transmission, collision.
The objective is to design a transmission protocol that coordinates the users'
transmissions and achieves high throughput and low delay.
We present a decentralized Common Information-Based Multiple Access (CIMA)
protocol that has the following features: (i) it achieves the full throughput
region of the collision channel; (ii) it results in a delay that is linear in
the number of users, and is significantly lower than that of CSMA protocols;
(iii) it avoids collisions without channel sensing
Optimal User-Cell Association for Massive MIMO Wireless Networks
The use of a very large number of antennas at each base station site
(referred to as "Massive MIMO") is one of the most promising approaches to cope
with the predicted wireless data traffic explosion. In combination with Time
Division Duplex and with simple per-cell processing, it achieves large
throughput per cell, low latency, and attractive power efficiency performance.
Following the current wireless technology trend of moving to higher frequency
bands and denser small cell deployments, a large number of antennas can be
implemented within a small form factor even in small cell base stations. In a
heterogeneous network formed by large (macro) and small cell BSs, a key system
optimization problem consists of "load balancing", that is, associating users
to BSs in order to avoid congested hot-spots and/or under-utilized
infrastructure. In this paper, we consider the user-BS association problem for
a massive MIMO heterogeneous network. We formulate the problem as a network
utility maximization, and provide a centralized solution in terms of the
fraction of transmission resources (time-frequency slots) over which each user
is served by a given BS. Furthermore, we show that such a solution is
physically realizable, i.e., there exists a sequence of integer scheduling
configurations realizing (by time-sharing) the optimal fractions. While this
solution is optimal, it requires centralized computation. Then, we also
consider decentralized user-centric schemes, formulated as non-cooperative
games where each user makes individual selfish association decisions based only
on its local information. We identify a class of schemes such that their Nash
equilibrium is very close to the global centralized optimum. Hence, these
user-centric algorithms are attractive not only for their simplicity and fully
decentralized implementation, but also because they operate near the system
"social" optimum
Decentralized Fair Scheduling in Two-Hop Relay-Assisted Cognitive OFDMA Systems
In this paper, we consider a two-hop relay-assisted cognitive downlink OFDMA
system (named as secondary system) dynamically accessing a spectrum licensed to
a primary network, thereby improving the efficiency of spectrum usage. A
cluster-based relay-assisted architecture is proposed for the secondary system,
where relay stations are employed for minimizing the interference to the users
in the primary network and achieving fairness for cell-edge users. Based on
this architecture, an asymptotically optimal solution is derived for jointly
controlling data rates, transmission power, and subchannel allocation to
optimize the average weighted sum goodput where the proportional fair
scheduling (PFS) is included as a special case. This solution supports
decentralized implementation, requires small communication overhead, and is
robust against imperfect channel state information at the transmitter (CSIT)
and sensing measurement. The proposed solution achieves significant throughput
gains and better user-fairness compared with the existing designs. Finally, we
derived a simple and asymptotically optimal scheduling solution as well as the
associated closed-form performance under the proportional fair scheduling for a
large number of users. The system throughput is shown to be
, where is the
number of users in one cluster, is the number of subchannels and is
the active probability of primary users.Comment: 29 pages, 9 figures, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL
PROCESSIN
Almost Optimal Channel Access in Multi-Hop Networks With Unknown Channel Variables
We consider distributed channel access in multi-hop cognitive radio networks.
Previous works on opportunistic channel access using multi-armed bandits (MAB)
mainly focus on single-hop networks that assume complete conflicts among all
secondary users. In the multi-hop multi-channel network settings studied here,
there is more general competition among different communication pairs. We
formulate the problem as a linearly combinatorial MAB problem that involves a
maximum weighted independent set (MWIS) problem with unknown weights which need
to learn. Existing methods for MAB where each of nodes chooses from
channels have exponential time and space complexity , and poor
theoretical guarantee on throughput performance. We propose a distributed
channel access algorithm that can achieve of the optimum averaged
throughput where each node has communication complexity and space
complexity in the learning process, and time complexity in strategy decision process for an arbitrary wireless network.
Here is the approximation ratio to MWIS for a local -hop
network with nodes,and is the number of mini-rounds inside each round
of strategy decision. For randomly located networks with an average degree ,
the time complexity is .Comment: 9 page
- …