اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدMessage Routing in Wireless and Mobile Networks using TDMA Technology
155
0
0.0
(
0
)
نشر من قبل
Timotheos Aslanidis
تاريخ النشر
2016
مجال البحث
الهندسة المعلوماتية
والبحث باللغة
English
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In an era where communication has a most important role in modern societies, designing efficient algorithms for data transmission is of the outmost importance. TDMA is a technology used in many communication systems such as satellite, cell phone as well as other wireless or mobile networks. Most 2G cellular systems as well as some 3G are TDMA based. In order to transmit data in such systems we need to cluster them in packages. To achieve a faster transmission we are allowed to preempt the transmission of any packet in order to resume at a later time. Preemption can be used to reduce idleness of some stations. Such preemptions though come with a reconfiguration cost in order to setup for the next transmission. In this paper we propose two algorithms which yield improved transmission scheduling. These two algorithms we call MGA and IMGA (Improved MGA). We have proven an approximation ratio for MGA and ran experiments to establish that it works even better in practice. In order to conclude that MGA will be a very helpful tool in constructing an improved schedule for packet routing using preemtion with a setup cost, we compare its results to two other efficient algorithms designed by researchers in the past: A-PBS(d+1) and GWA. To establish the efficiency of IMGA we ran experiments in comparison to MGA as well as A-PBS(d+1) and GWA. IMGA has proven to produce the most efficient schedule on all counts.
قيم البحث
اقرأ أيضاً
We consider a dense, ad hoc wireless network, confined to a small region. The wireless network is operated as a single cell, i.e., only one successful transmission is supported at a time. Data packets are sent between sourcedestination pairs by multi
hop relaying. We assume that nodes self-organise into a multihop network such that all hops are of length d meters, where d is a design parameter. There is a contention based multiaccess scheme, and it is assumed that every node always has data to send, either originated from it or a transit packet (saturation assumption). In this scenario, we seek to maximize a measure of the transport capacity of the network (measured in bit-meters per second) over power controls (in a fading environment) and over the hop distance d, subject to an average power constraint. We first argue that for a dense collection of nodes confined to a small region, single cell operation is efficient for single user decoding transceivers. Then, operating the dense ad hoc wireless network (described above) as a single cell, we study the hop length and power control that maximizes the transport capacity for a given network power constraint.
Traffic load balancing and resource allocation is set to play a crucial role in leveraging the dense and increasingly heterogeneous deployment of multi-radio wireless networks. Traffic aggregation across different access points (APs)/radio access tec
hnologies (RATs) has become an important feature of recently introduced cellular standards on LTE dual connectivity and LTE-WLAN aggregation (LWA). Low complexity traffic splitting solutions for scenarios where the APs are not necessarily collocated are of great interest for operators. In this paper, we consider a scenario, where traffic for each user may be split across macrocell and an LTE or WiFi small cells connected by non-ideal backhaul links, and develop a closed form solution for optimal aggregation accounting for the backhaul delay. The optimal solution lends itself to a water-filling based interpretation, where the fraction of users traffic sent over macrocell is proportional to ratio of users peak capacity on that macrocell and its throughput on the small cell. Using comprehensive system level simulations, the developed optimal solution is shown to provide substantial edge and median throughput gain over algorithms representative of current 3GPP-WLAN interworking solutions. The achievable performance benefits hold promise for operators expecting to introduce aggregation solutions with their existing WLAN deployments.
Traffic load balancing and radio resource management is key to harness the dense and increasingly heterogeneous deployment of next generation $5$G wireless infrastructure. Strategies for aggregating user traffic from across multiple radio access tech
nologies (RATs) and/or access points (APs) would be crucial in such heterogeneous networks (HetNets), but are not well investigated. In this paper, we develop a low complexity solution for maximizing an $alpha$-optimal network utility leveraging the multi-link aggregation (simultaneous connectivity to multiple RATs/APs) capability of users in the network. The network utility maximization formulation has maximization of sum rate ($alpha=0$), maximization of minimum rate ($alpha to infty$), and proportional fair ($alpha=1$) as its special cases. A closed form is also developed for the special case where a user aggregates traffic from at most two APs/RATs, and hence can be applied to practical scenarios like LTE-WLAN aggregation (LWA) and LTE dual-connectivity solutions. It is shown that the required objective may also be realized through a decentralized implementation requiring a series of message exchanges between the users and network. Using comprehensive system level simulations, it is shown that optimal leveraging of multi-link aggregation leads to substantial throughput gains over single RAT/AP selection techniques.
In mobile crowd sensing networks data forwarding through opportunistic contacts between participants. Data is replicated to encountered participants. For optimizing data delivery ratio and reducing redundant data a lot of data forwarding schemes, whi
ch selectively replicate data to encountered participants through nodes data forwarding metric are proposed. However most of them neglect a kind of redundant data whose Time-To-Live is expired. For reducing this kind of redundant data we proposed a new method to evaluate nodes data forwarding metric, which is used to measure the nodes probability of forwarding data to destination within datas constraint time. The method is divided into two parts. The first is evaluating nodes whether have possibility to contact destination within time constraint based on transient cluster. We propose a method to detect nodes transient cluster, which is based on nodes contact rate. Only node, which has possibility to contact destination, has chances to the second step. It effectively reduces the computational complexity. The second is calculating data forwarding probability of node to destination according to individual ICT (inter contact time) distribution. Evaluation results show that our proposed transient cluster detection method is more simple and quick. And from two aspects of data delivery ratio and network overhead our approach outperforms other existing data forwarding approach.
A fundamental problem arising in dense wireless networks is the high co-channel interference. Interference alignment (IA) was recently proposed as an effective way to combat interference in wireless networks. The concept of IA, though, is originated
by the capacity study of interference channels and as such, its performance is mainly gauged under ideal assumptions, such as instantaneous and perfect channel state information (CSI) at all nodes, and homogeneous signal-to-noise ratio (SNR) users, i.e., each user has the same average SNR. Consequently, the performance of IA under realistic conditions has not been completely investigated yet. In this paper, we aim at filling this gap by providing a performance assessment of spatial IA in practical systems. Specifically, we derive a closed-form expression for the IA average sum-rate when CSI is acquired through training and users have heterogeneous SNR. A main insight from our analysis is that IA can indeed provide significant spectral efficiency gains over traditional approaches in a wide range of dense network scenarios. To demonstrate this, we consider the examples of linear, grid and random network topologies.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
