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Register a new userProportional Fair Traffic Splitting and Aggregation in Heterogeneous Wireless Networks
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Added by
Sarabjot Singh
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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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 technologies (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.
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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 technologies (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.
Future wireless networks will be characterized by heterogeneous traffic requirements. Such requirements can be low-latency or minimum-throughput. Therefore, the network has to adjust to different needs. Usually, users with low-latency requirements have to deliver their demand within a specific time frame, i.e., before a deadline, and they co-exist with throughput oriented users. In addition, the users are mobile and they share the same wireless channel. Therefore, they have to adjust their power transmission to achieve reliable communication. However, due to the limited power budget of wireless mobile devices, a power-efficient scheduling scheme is required by the network. In this work, we cast a stochastic network optimization problem for minimizing the packet drop rate while guaranteeing a minimum throughput and taking into account the limited-power capabilities of the users. We apply tools from Lyapunov optimization theory in order to provide an algorithm, named Dynamic Power Control (DPC) algorithm, that solves the formulated problem in realtime. It is proved that the DPC algorithm gives a solution arbitrarily close to the optimal one. Simulation results show that our algorithm outperforms the baseline Largest-Debt-First (LDF) algorithm for short deadlines and multiple users.
We study and compare three coded schemes for single-server wireless broadcast of multiple description coded content to heterogeneous users. The users (sink nodes) demand different number of descriptions over links with different packet loss rates. The three coded schemes are based on the LT codes, growth codes, and randomized chunked codes. The schemes are compared on the basis of the total number of transmissions required to deliver the demands of all users, which we refer to as the server (source) delivery time. We design the degree distributions of LT codes by solving suitably defined linear optimization problems, and numerically characterize the achievable delivery time for different coding schemes. We find that including a systematic phase (uncoded transmission) is significantly beneficial for scenarios with low demands, and that coding is necessary for efficiently delivering high demands. Different demand and error rate scenarios may require very different coding schemes. Growth codes and chunked codes do not perform as well as optimized LT codes in the heterogeneous communication scenario.
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.
Cell association scheme determines which base station (BS) and mobile user (MU) should be associated with and also plays a significant role in determining the average data rate a MU can achieve in heterogeneous networks. However, the explosion of digital devices and the scarcity of spectra collectively force us to carefully re-design cell association scheme which was kind of taken for granted before. To address this, we develop a new cell association scheme in heterogeneous networks based on joint consideration of the signal-to-interference-plus-noise ratio (SINR) which a MU experiences and the traffic load of candidate BSs1. MUs and BSs in each tier are modeled as several independent Poisson point processes (PPPs) and all channels experience independently and identically distributed ( i.i.d.) Rayleigh fading. Data rate ratio and traffic load ratio distributions are derived to obtain the tier association probability and the average ergodic MU data rate. Through numerical results, We find that our proposed cell association scheme outperforms cell range expansion (CRE) association scheme. Moreover, results indicate that allocating small sized and high-density BSs will improve spectral efficiency if using our proposed cell association scheme in heterogeneous networks.
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