515 research outputs found
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
Self-organised multi-objective network clustering for coordinated communications in future wireless networks
The fifth generation (5G) cellular system is being developed with a vision of 1000 times more capacity than the fourth generation (4G) systems to cope with ever increasing mobile data traffic. Interference mitigation plays an important role in improving the much needed overall capacity especially in highly interference-limited dense deployment scenarios envisioned for 5G. Coordinated multi-point (CoMP) is identified as a promising interference mitigation technique where multiple base stations (BS) can cooperate for joint transmission/reception by exchanging user/control data and perform joint signal processing to mitigate inter-cell interference and even exploit it as a useful signal. CoMP is already a key feature of long term evolution-advanced (LTE-A) and envisioned as an essential function for 5G. However, CoMP cannot be realized for the whole network due to its computational complexity, synchronization requirement between coordinating BSs and high backhaul capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters.
This PhD thesis aims to investigate optimum dynamic CoMP clustering solutions in 5G and beyond wireless networks with massive small cell (SC) deployment. Truly self-organised CoMP clustering algorithms are investigated, aiming to improve much needed spectral efficiency and other network objectives especially load balancing in future wireless networks. Low complexity, scalable, stable and efficient CoMP clustering algorithms are designed to jointly optimize spectral efficiency, load balancing and limited backhaul availability.
Firstly, we provide a self organizing, load aware, user-centric CoMP clustering algorithm in a control and data plane separation architecture (CDSA) proposed for 5G to maximize spectral efficiency and improve load balancing. We introduce a novel re-clustering algorithm for user equipment (UE) served by highly loaded cells and show that unsatisfied UEs due to high load can be significantly reduced with minimal impact on spectral efficiency. Clustering with load balancing algorithm exploits the capacity gain from increase in cluster size and also the traffic shift from highly loaded cells to lightly loaded neighbours.
Secondly, we develop a novel, low complexity, stable, network-centric clustering model to jointly optimize load balancing and spectral efficiency objectives and tackle the complexity and scalability issues of user-centric clustering. We show that our clustering model provide high spectral efficiency in low-load scenario and better load distribution in high-load scenario resulting in lower number of unsatisfied users while keeping spectral efficiency at comparably high levels. Unsatisfied UEs due to high load are reduced by with our algorithm when compared to greedy clustering model. In this context, the unique contribution of this work that it is the first attempt to fill the gap in literature for multi-objective, network-centric CoMP clustering, jointly optimizing load balancing and spectral efficiency.
Thirdly, we design a novel multi-objective CoMP clustering algorithm to include backhaul-load awareness and tackle one of the biggest challenges for the realization of CoMP in future networks i.e. the demand for high backhaul bandwidth and very low latency. We fill the gap in literature as the first attempt to design a clustering algorithm to jointly optimize backhaul/radio access load and spectral efficiency and analyze the trade-off between them. We employ 2 novel coalitional game theoretic clustering methods, 1-a novel merge/split/transfer coalitional game theoretic clustering algorithm to form backhaul and load aware BS clusters where spectral efficiency is still kept at high level, 2-a novel user transfer game model to move users between clusters to improve load balancing further. Stability and complexity analysis is provided and simulation results are presented to show the performance of the proposed method under different backhaul availability scenarios. We show that average system throughout is increased by 49.9% with our backhaul-load aware model in high load scenario when compared to a greedy model.
Finally, we provide an operator's perspective on deployment of CoMP. Firstly, we present the main motivation and benefits of CoMP from an operator's viewpoint. Next, we present operational requirements for CoMP implementation and discuss practical considerations and challenges of such deployment. Possible solutions for these experienced challenges are reviewed. We then present initial results from a UL CoMP trial and discuss changes in key network performance indicators (KPI) during the trial. Additionally, we propose further improvements to the trialed CoMP scheme for better potential gains and give our perspective on how CoMP will fit into the future wireless networks
Will 5G See its Blind Side? Evolving 5G for Universal Internet Access
Internet has shown itself to be a catalyst for economic growth and social
equity but its potency is thwarted by the fact that the Internet is off limits
for the vast majority of human beings. Mobile phones---the fastest growing
technology in the world that now reaches around 80\% of humanity---can enable
universal Internet access if it can resolve coverage problems that have
historically plagued previous cellular architectures (2G, 3G, and 4G). These
conventional architectures have not been able to sustain universal service
provisioning since these architectures depend on having enough users per cell
for their economic viability and thus are not well suited to rural areas (which
are by definition sparsely populated). The new generation of mobile cellular
technology (5G), currently in a formative phase and expected to be finalized
around 2020, is aimed at orders of magnitude performance enhancement. 5G offers
a clean slate to network designers and can be molded into an architecture also
amenable to universal Internet provisioning. Keeping in mind the great social
benefits of democratizing Internet and connectivity, we believe that the time
is ripe for emphasizing universal Internet provisioning as an important goal on
the 5G research agenda. In this paper, we investigate the opportunities and
challenges in utilizing 5G for global access to the Internet for all (GAIA). We
have also identified the major technical issues involved in a 5G-based GAIA
solution and have set up a future research agenda by defining open research
problems
5G Ultra-dense networks with non-uniform Distributed Users
User distribution in ultra-dense networks (UDNs) plays a crucial role in
affecting the performance of UDNs due to the essential coupling between the
traffic and the service provided by the networks. Existing studies are mostly
based on the assumption that users are uniformly distributed in space. The
non-uniform user distribution has not been widely considered despite that it is
much closer to the real scenario. In this paper, Radiation and Absorbing model
(R&A model) is first adopted to analyze the impact of the non-uniformly
distributed users on the performance of 5G UDNs. Based on the R&A model and
queueing network theory, the stationary user density in each hot area is
investigated. Furthermore, the coverage probability, network throughput and
energy efficiency are derived based on the proposed theoretical model. Compared
with the uniformly distributed assumption, it is shown that non-uniform user
distribution has a significant impact on the performance of UDNs.Comment: 14 pages, 10 figure
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