31 research outputs found
Generalized Instantly Decodable Network Coding for Relay-Assisted Networks
In this paper, we investigate the problem of minimizing the frame completion
delay for Instantly Decodable Network Coding (IDNC) in relay-assisted wireless
multicast networks. We first propose a packet recovery algorithm in the single
relay topology which employs generalized IDNC instead of strict IDNC previously
proposed in the literature for the same relay-assisted topology. This use of
generalized IDNC is supported by showing that it is a super-set of the strict
IDNC scheme, and thus can generate coding combinations that are at least as
efficient as strict IDNC in reducing the average completion delay. We then
extend our study to the multiple relay topology and propose a joint generalized
IDNC and relay selection algorithm. This proposed algorithm benefits from the
reception diversity of the multiple relays to further reduce the average
completion delay in the network. Simulation results show that our proposed
solutions achieve much better performance compared to previous solutions in the
literature.Comment: 5 pages, IEEE PIMRC 201
Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications
From its introduction to its quindecennial, network coding has built a strong reputation for enhancing packet recovery and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the system by proposing elaborate schemes able to reach the network capacity. With the shift toward distributed computing on mobile devices, performance and complexity become both critical factors that affect the efficiency of a coding strategy. Instantly decodable network coding presents itself as a new paradigm in network coding that trades off these two aspects. This paper review instantly decodable network coding schemes by identifying, categorizing, and evaluating various algorithms proposed in the literature. The first part of the manuscript investigates the conventional centralized systems, in which all decisions are carried out by a central unit, e.g., a base-station. In particular, two successful approaches known as the strict and generalized instantly decodable network are compared in terms of reliability, performance, complexity, and packet selection methodology. The second part considers the use of instantly decodable codes in a device-to-device communication network, in which devices speed up the recovery of the missing packets by exchanging network coded packets. Although the performance improvements are directly proportional to the computational complexity increases, numerous successful schemes from both the performance and complexity viewpoints are identified
Instantly Decodable Network Coding: From Point to Multi-Point to Device-to-Device Communications
The network coding paradigm enhances transmission efficiency by
combining information
flows and has drawn significant attention in information theory,
networking, communications
and data storage. Instantly decodable network coding (IDNC), a
subclass of network coding,
has demonstrated its ability to improve the quality of service of
time critical applications
thanks to its attractive properties, namely the throughput
enhancement, delay reduction,
simple XOR-based encoding and decoding, and small coefficient
overhead. Nonetheless, for
point to multi-point (PMP) networks, IDNC cannot guarantee the
decoding of a specific new
packet at individual devices in each transmission. Furthermore,
for device-to-device (D2D)
networks, the transmitting devices may possess only a subset of
packets, which can be used
to form coded packets. These challenges require the optimization
of IDNC algorithms to be
suitable for different application requirements and network
configurations.
In this thesis, we first study a scalable live video broadcast
over a wireless PMP network,
where the devices receive video packets from a base station. Such
layered live video has a
hard deadline and imposes a decoding order on the video layers.
We design two prioritized
IDNC algorithms that provide a high level of priority to the most
important video layer
before considering additional video layers in coding decisions.
These prioritized algorithms
are shown to increase the number of decoded video layers at the
devices compared to the
existing network coding schemes.
We then study video distribution over a partially connected D2D
network, where a group
of devices cooperate with each other to recover their missing
video content. We introduce
a cooperation aware IDNC graph that defines all feasible coding
and transmission conflictfree
decisions. Using this graph, we propose an IDNC solution that
avoids coding and
transmission conflicts, and meets the hard deadline for high
importance video packets. It is
demonstrated that the proposed solution delivers an improved
video quality to the devices
compared to the video and cooperation oblivious coding schemes.
We also consider a heterogeneous network wherein devices use two
wireless interfaces to
receive packets from the base station and another device
concurrently. For such network,
we are interested in applications with reliable in-order packet
delivery requirements. We
represent all feasible coding opportunities and conflict-free
transmissions using a dual interface
IDNC graph. We select a maximal independent set over the graph by
considering dual
interfaces of individual devices, in-order delivery requirements
of packets and lossy channel
conditions. This graph based solution is shown to reduce the
in-order delivery delay
compared to the existing network coding schemes.
Finally, we consider a D2D network with a group of devices
experiencing heterogeneous
channel capacities. For such cooperative scenarios, we address
the problem of minimizing
the completion time required for recovering all missing packets
at the devices using IDNC
and physical layer rate adaptation. Our proposed IDNC algorithm
balances between the
adopted transmission rate and the number of targeted devices that
can successfully receive
the transmitted packet. We show that the proposed rate aware IDNC
algorithm reduces the
completion time compared to the rate oblivious coding scheme
Design and analysis of network coding schemes for efficient fronthaul offloading of fog-radio access networks
In the era of the Internet of Things (IoT), everything will be connected. Smart homes and cities, connected cars, smart agriculture, wearable technologies, smart healthcare, smart sport, and fitness are all becoming a reality. However, the current cloud architecture cannot manage the tremendous number of connected devices and skyrocketing data traffic while providing the speeds promised by 5G and beyond. Centralised cloud data centres are physically too far from where the data originate (edge of the network), inevitably leading to data transmission speeds that are too slow for delay-sensitive applications. Thus, researchers have proposed fog architecture as a solution to the ever-increasing number of connected devices and data traffic.
The main idea of fog architecture is to bring content physically closer to end users, thus reducing data transmission times. This thesis considers a type of fog architecture in which smart end devices have storage and processing capabilities and can communicate and collaborate with each other. The major goal of this thesis is to develop methods of efficiently governing communication and collaboration between smart end devices so that their requests to upper network layers are minimised. This is achieved by incorporating principles from graph theory, network coding and machine learning to model the problem and design efficient network-coded scheduling algorithms to further enhance achieved performance. By maximising end users' self-sufficiency, the load on the system is decreased and its capacity increased. This will allow the central processing unit to manage more devices which is vital, given that more than 29 billion devices will connect to the infrastructure by 2023 \cite{Cisco1823}.
Specifically, given that the limitations of the smart end devices and the system in general lead to various communication conflicts, a novel network coding graph is developed that takes into account all possible conflicts and enables the search for an efficient feasible solution. The thesis designs heuristic algorithms that search for the solution over the novel network coding graph, investigates the complexity of the proposed algorithms, and shows the offloading strategy's asymptotic optimality.
Although the main aim of this work is to decrease the involvement of upper fog layers in serving smart end devices, it also takes into account how much energy end devices would use during collaborations. Unfortunately, a higher system capacity comes at the price of more energy spent by smart end devices; thus, service providers' interests and end users' interests are conflicting. Finally, this thesis investigates how multihop communication between end devices influences the offloading of upper fog layers. Smart end devices are equipped with machine learning capabilities that allow them to find efficient paths to their peers, further improving offloading.
In conclusion, the work in this thesis shows that by smartly designing and scheduling communication between end devices, it is possible to significantly reduce the load on the system, increase its capacity and achieve fast transmissions between end devices, allowing them to run latency-critical applications
Low Complexity Scheduling and Coding for Wireless Networks
The advent of wireless communication technologies has created a paradigm shift in the accessibility of communication. With it has come an increased demand for throughput, a trend that is likely to increase further in the future. A key aspect of these challenges is to develop low complexity algorithms and architectures that can take advantage of the nature of the wireless medium like broadcasting and physical layer cooperation. In this thesis, we consider several problems in the domain of low complexity coding, relaying and scheduling for wireless networks. We formulate the Pliable Index Coding problem that models a server trying to send one or more new messages over a noiseless broadcast channel to a set of clients that already have a subset of messages as side information. We show through theoretical bounds and algorithms, that it is possible to design short length codes, poly-logarithmic in the number of clients, to solve this problem. The length of the codes are exponentially better than those possible in a traditional index coding setup. Next, we consider several aspects of low complexity relaying in half-duplex diamond networks. In such networks, the source transmits information to the destination through half-duplex intermediate relays arranged in a single layer. The half-duplex nature of the relays implies that they can either be in a listening or transmitting state at any point of time. To achieve high rates, there is an additional complexity of optimizing the schedule (i.e. the relative time fractions) of the relaying states, which can be in number. Using approximate capacity expressions derived from the quantize-map-forward scheme for physical layer cooperation, we show that for networks with relays, the optimal schedule has atmost active states. This is an exponential improvement over the possible active states in a schedule. We also show that it is possible to achieve at least half the capacity of such networks (approximately) by employing simple routing strategies that use only two relays and two scheduling states. These results imply that the complexity of relaying in half-duplex diamond networks can be significantly reduced by using fewer scheduling states or fewer relays without adversely affecting throughput. Both these results assume centralized processing of the channel state information of all the relays. We take the first steps in analyzing the performance of relaying schemes where each relay switches between listening and transmitting states randomly and optimizes their relative fractions using only local channel state information. We show that even with such simple scheduling, we can achieve a significant fraction of the capacity of the network. Next, we look at the dual problem of selecting the subset of relays of a given size that has the highest capacity for a general layered full-duplex relay network. We formulate this as an optimization problem and derive efficient approximation algorithms to solve them. We end the thesis with the design and implementation of a practical relaying scheme called QUILT. In it the relay opportunistically decodes or quantizes its received signal and transmits the resulting sequence in cooperation with the source. To keep the complexity of the system low, we use LDPC codes at the source, interleaving at the relays and belief propagation decoding at the destination. We evaluate our system through testbed experiments over WiFi