43 research outputs found
Energy sustainable paradigms and methods for future mobile networks: A survey
In this survey, we discuss the role of energy in the design of future mobile
networks and, in particular, we advocate and elaborate on the use of energy
harvesting (EH) hardware as a means to decrease the environmental footprint of
5G technology. To take full advantage of the harvested (renewable) energy,
while still meeting the quality of service required by dense 5G deployments,
suitable management techniques are here reviewed, highlighting the open issues
that are still to be solved to provide eco-friendly and cost-effective mobile
architectures. Several solutions have recently been proposed to tackle
capacity, coverage and efficiency problems, including: C-RAN, Software Defined
Networking (SDN) and fog computing, among others. However, these are not
explicitly tailored to increase the energy efficiency of networks featuring
renewable energy sources, and have the following limitations: (i) their energy
savings are in many cases still insufficient and (ii) they do not consider
network elements possessing energy harvesting capabilities. In this paper, we
systematically review existing energy sustainable paradigms and methods to
address points (i) and (ii), discussing how these can be exploited to obtain
highly efficient, energy self-sufficient and high capacity networks. Several
open issues have emerged from our review, ranging from the need for accurate
energy, transmission and consumption models, to the lack of accurate data
traffic profiles, to the use of power transfer, energy cooperation and energy
trading techniques. These challenges are here discussed along with some
research directions to follow for achieving sustainable 5G systems.Comment: Accepted by Elsevier Computer Communications, 21 pages, 9 figure
Low Complexity Delay-Constrained Beamforming for Multi-User MIMO Systems with Imperfect CSIT
In this paper, we consider the delay-constrained beamforming control for
downlink multi-user MIMO (MU- MIMO) systems with imperfect channel state
information at the transmitter (CSIT). The delay-constrained control problem is
formulated as an infinite horizon average cost partially observed Markov
decision process. To deal with the curse of dimensionality, we introduce a
virtual continuous time system and derive a closed-form approximate value
function using perturbation analysis w.r.t. the CSIT errors. To deal with the
challenge of the conditional packet error rate (PER), we build a tractable
closed- form approximation using a Bernstein-type inequality. Based on the
closed-form approximations of the relative value function and the conditional
PER, we propose a conservative formulation of the original beamforming control
problem. The conservative problem is non-convex and we transform it into a
convex problem using the semidefinite relaxation (SDR) technique. We then
propose an alternating iterative algorithm to solve the SDR problem. Finally,
the proposed scheme is compared with various baselines through simulations and
it is shown that significant performance gain can be achieved.Comment: 14 pages, 7 figures, 1 table. This paper has been accepted by the
IEEE Transactions on Signal Processin
Joint Content Delivery and Caching Placement via Dynamic Programming
In this paper, downlink delivery of popular content is optimized with the
assistance of wireless cache nodes. Specifically, the requests of one file is
modeled as a Poisson point process with finite lifetime, and two downlink
transmission modes are considered: (1) the base station multicasts file
segments to the requesting users and selected cache nodes; (2) the base station
proactively multicasts file segments to the selected cache nodes without
requests from users. Hence the cache nodes with decoded files can help to
offload the traffic upon the next file request via other air interfaces, e.g.
WiFi. Without proactive caching placement, we formulate the downlink traffic
offloading as a Markov decision process with random number of stages, and
propose a revised Bellman's equation to obtain the optimal control policy. In
order to address the prohibitively huge state space, we also introduce a
low-complexity sub-optimal solution based on linear approximation of the value
functions, where the gap between the approximated value functions and the real
ones is bounded analytically. The approximated value functions can be
calculated from analytical expressions given the spatial distribution of
requesting users. Moreover, we propose a learning-based algorithm to evaluate
the approximated value functions for unknown distribution of requesting users.
Finally, a proactive caching placement algorithm is introduced to exploit the
temporal diversity of shadowing effect. It is shown by simulation that the
proposed low-complexity algorithm based on approximated value functions can
significantly reduce the resource consumption at the base station, and the
proactive caching placement can further improve the performance
Dynamic Virtual Resource Allocation for 5G and Beyond Network Slicing
The fifth generation and beyond wireless communication will support vastly
heterogeneous services and use demands such as massive connection, low latency
and high transmission rate. Network slicing has been envisaged as an efficient
technology to meet these diverse demands. In this paper, we propose a dynamic
virtual resources allocation scheme based on the radio access network (RAN)
slicing for uplink communications to ensure the quality-of-service (QoS). To
maximum the weighted-sum transmission rate performance under delay constraint,
formulate a joint optimization problem of subchannel allocation and power
control as an infinite-horizon average-reward constrained Markov decision
process (CMDP) problem. Based on the equivalent Bellman equation, the optimal
control policy is first derived by the value iteration algorithm. However, the
optimal policy suffers from the widely known curse-of-dimensionality problem.
To address this problem, the linear value function approximation (approximate
dynamic programming) is adopted. Then, the subchannel allocation Q-factor is
decomposed into the per-slice Q-factor. Furthermore, the Q-factor and
Lagrangian multipliers are updated by the use of an online stochastic learning
algorithm. Finally, simulation results reveal that the proposed algorithm can
meet the delay requirements and improve the user transmission rate compared
with baseline schemes
D4.3 Final Report on Network-Level Solutions
Research activities in METIS reported in this document focus on proposing solutions
to the network-level challenges of future wireless communication networks. Thereby, a large variety of scenarios is considered and a set of technical concepts is proposed to serve the needs envisioned for the 2020 and beyond.
This document provides the final findings on several network-level aspects and groups of
solutions that are considered essential for designing future 5G solutions. Specifically, it
elaborates on:
-Interference management and resource allocation schemes
-Mobility management and robustness enhancements
-Context aware approaches
-D2D and V2X mechanisms
-Technology components focused on clustering
-Dynamic reconfiguration enablers
These novel network-level technology concepts are evaluated against requirements defined
by METIS for future 5G systems. Moreover, functional enablers which can support the
solutions mentioned aboveare proposed.
We find that the network level solutions and technology components developed during the course of METIS complement the lower layer technology components and thereby effectively contribute to meeting 5G requirements and targets.Aydin, O.; Valentin, S.; Ren, Z.; Botsov, M.; Lakshmana, TR.; Sui, Y.; Sun, W.... (2015). D4.3 Final Report on Network-Level Solutions. http://hdl.handle.net/10251/7675
Next-generation Wireless Solutions for the Smart Factory, Smart Vehicles, the Smart Grid and Smart Cities
5G wireless systems will extend mobile communication services beyond mobile
telephony, mobile broadband, and massive machine-type communication into new
application domains, namely the so-called vertical domains including the smart
factory, smart vehicles, smart grid, smart city, etc. Supporting these vertical
domains comes with demanding requirements: high-availability, high-reliability,
low-latency, and in some cases, high-accuracy positioning. In this survey, we
first identify the potential key performance requirements of 5G communication
in support of automation in the vertical domains and highlight the 5G enabling
technologies conceived for meeting these requirements. We then discuss the key
challenges faced both by industry and academia which have to be addressed in
order to support automation in the vertical domains. We also provide a survey
of the related research dedicated to automation in the vertical domains.
Finally, our vision of 6G wireless systems is discussed briefly
Energy aware management of 5G networks
Doctor of PhilosophyDepartment of Electrical and Computer EngineeringBalasubramaniam NatarajanThe number of wireless devices is predicted to skyrocket from about 5 billion in 2015 to 25 billion by 2020. Therefore, traffic volume demand is envisioned to explode in the very near future. The proposed fifth generation (5G) of mobile networks is expected to be a mixture of network components with different sizes, transmit powers, back-haul connections and radio access technologies. While there are many interesting problems within the 5G framework, we address the challenges of energy-related management in a heterogeneous 5G networks. Based on the 5G architecture, in this dissertation, we present some fundamental methodologies to analyze and improve the energy efficiency of 5G network components using mathematical tools from optimization, control theory and stochastic geometry.
Specifically, the main contributions of this research include:
• We design power-saving modes in small cells to maximize energy efficiency. We first derive performance metrics for heterogeneous cellular networks with sleep modes based on stochastic geometry. Then we quantify the energy efficiency and maximize it with quality-of-service constraint based on an analytical model. We also develop a simple sleep strategy to further improve the energy efficiency according to traffic conditions.
• We conduct a techno-economic analysis of heterogeneous cellular networks powered by both on-grid electricity and renewable energy. We propose a scheme to minimize the electricity cost based on a real-time pricing model.
• We provide a framework to uncover desirable system design parameters that offer the best gains in terms of ergodic capacity and average achievable throughput for device-to-device underlay cellular networks. We also suggest a two-phase scheme to optimize the ergodic capacity while minimizing the total power consumption.
• We investigate the modeling and analysis of simultaneous information and energy transfer in Internet of things and evaluate both transmission outage probability and power outage probability. Then we try to balance the trade-off between the outage performances by careful design of the power splitting ratio.
This research provides valuable insights related to the trade-offs between energy-conservation and system performance in 5G networks. Theoretical and simulation results help verify the performance of the proposed algorithms
Online energy efficient packet scheduling for a common deadline with and without energy harvesting
The problem of online packet scheduling to minimize the required conventional
grid energy for transmitting a fixed number of packets given a common deadline
is considered. The total number of packets arriving within the deadline is
known, but the packet arrival times are unknown, and can be arbitrary. The
proposed algorithm tries to finish the transmission of each packet assuming all
future packets are going to arrive at equal time intervals within the left-over
time. The proposed online algorithm is shown to have competitive ratio that is
logarithmic in the number of packet arrivals. The hybrid energy paradigm is
also considered, where in addition to grid energy, energy is also available via
extraction from renewable sources. The objective here is to minimize the grid
energy use. A suitably modified version of the previous algorithm is also shown
to have competitive ratio that is logarithmic in the number of packet arrivals
Optimal Power Allocation for Energy Harvesting and Power Grid Coexisting Wireless Communication Systems
This paper considers the power allocation of a single-link wireless
communication with joint energy harvesting and grid power supply. We formulate
the problem as minimizing the grid power consumption with random energy and
data arrival, and analyze the structure of the optimal power allocation policy
in some special cases. For the case that all the packets are arrived before
transmission, it is a dual problem of throughput maximization, and the optimal
solution is found by the two-stage water filling (WF) policy, which allocates
the harvested energy in the first stage, and then allocates the power grid
energy in the second stage. For the random data arrival case, we first assume
grid energy or harvested energy supply only, and then combine the results to
obtain the optimal structure of the coexisting system. Specifically, the
reverse multi-stage WF policy is proposed to achieve the optimal power
allocation when the battery capacity is infinite. Finally, some heuristic
online schemes are proposed, of which the performance is evaluated by numerical
simulations.Comment: 29 pages, 11 figures, submitted to IEEE Transactions on
Communication
Traffic Adaptation and Energy Efficiency for Small Cell Networks with Dynamic TDD
The traffic in current wireless networks exhibits large variations in uplink (UL) and downlink (DL), which brings huge challenges to network operators in efficiently allocating radio resources. Dynamic time-division duplex (TDD) is considered as a promising scheme that is able to adjust the resource allocation to the instantaneous UL and DL traffic conditions, also known as traffic adaptation. In this work, we study how traffic adaptation and energy harvesting can improve the energy efficiency (EE) in multi-antenna small cell networks operating dynamic TDD. Given the queue length distribution of small cell access points (SAPs) and mobile users (MUs), we derive the optimal UL/DL configuration to minimize the service time of a typical small cell, and show that the UL/DL configuration that minimizes the service time also results in an optimal network EE, but does not necessarily achieve the optimal EE for SAP or MU individually. To further enhance the network EE, we provide SAPs with energy harvesting capabilities, and model the status of harvested energy at each SAP using a Markov chain. We derive the availability of the rechargeable battery under several battery utilization strategies, and observe that energy harvesting can significantly improve the network EE in the low traffic load regime. In summary, the proposed analytical framework allows us to elucidate the relationship between traffic adaptation and network EE in future dense networks with dynamic TDD. With this work, we quantify the potential benefits of traffic adaptation and energy harvesting in terms of service time and EE