56,753 research outputs found
Adaptive Multi-objective Optimization for Energy Efficient Interference Coordination in Multi-Cell Networks
In this paper, we investigate the distributed power allocation for multi-cell
OFDMA networks taking both energy efficiency and inter-cell interference (ICI)
mitigation into account. A performance metric termed as throughput contribution
is exploited to measure how ICI is effectively coordinated. To achieve a
distributed power allocation scheme for each base station (BS), the throughput
contribution of each BS to the network is first given based on a pricing
mechanism. Different from existing works, a biobjective problem is formulated
based on multi-objective optimization theory, which aims at maximizing the
throughput contribution of the BS to the network and minimizing its total power
consumption at the same time. Using the method of Pascoletti and Serafini
scalarization, the relationship between the varying parameters and minimal
solutions is revealed. Furthermore, to exploit the relationship an algorithm is
proposed based on which all the solutions on the boundary of the efficient set
can be achieved by adaptively adjusting the involved parameters. With the
obtained solution set, the decision maker has more choices on power allocation
schemes in terms of both energy consumption and throughput. Finally, the
performance of the algorithm is assessed by the simulation results.Comment: 29 page
Resource Allocation for Power Minimization in the Downlink of THP-based Spatial Multiplexing MIMO-OFDMA Systems
In this work, we deal with resource allocation in the downlink of spatial
multiplexing MIMO-OFDMA systems. In particular, we concentrate on the problem
of jointly optimizing the transmit and receive processing matrices, the channel
assignment and the power allocation with the objective of minimizing the total
power consumption while satisfying different quality-of-service requirements. A
layered architecture is used in which users are first partitioned in different
groups on the basis of their channel quality and then channel assignment and
transceiver design are sequentially addressed starting from the group of users
with most adverse channel conditions. The multi-user interference among users
belonging to different groups is removed at the base station using a
Tomlinson-Harashima pre-coder operating at user level. Numerical results are
used to highlight the effectiveness of the proposed solution and to make
comparisons with existing alternatives.Comment: 12 pages, 6 figures, IEEE Trans. Veh. Techno
Energy-Efficient Low-Complexity Algorithm in 5G Massive MIMO Systems
Energy efficiency (EE) is a critical design when taking into account
circuit power consumption (CPC) in fifth-generation cellular networks. These
problems arise because of the increasing number of antennas in massive
multiple-input multiple-output (MIMO) systems, attributable to inter-cell
interference for channel state information. Apart from that, a higher number
of radio frequency (RF) chains at the base station and active users consume
more power due to the processing activities in digital-to-analogue converters
and power amplifiers. Therefore, antenna selection, user selection, optimal
transmission power, and pilot reuse power are important aspects in improving
energy efficiency in massive MIMO systems. This work aims to investigate
joint antenna selection, optimal transmit power and joint user selection based
on deriving the closed-form of the maximal EE, with complete knowledge
of large-scale fading with maximum ratio transmission. It also accounts for
channel estimation and eliminating pilot contamination as antennasM→∞.
This formulates the optimization problem of joint optimal antenna selection,
transmits power allocation and joint user selection to mitigate inter-cellinterference
in downlink multi-cell massiveMIMO systems under minimized
reuse of pilot sequences based on a novel iterative low-complexity algorithm
(LCA) for Newton’s methods and Lagrange multipliers. To analyze the precise
power consumption, a novel power consumption scheme is proposed for
each individual antenna, based on the transmit power amplifier and CPC.
Simulation results demonstrate that the maximal EE was achieved using the
iterative LCA based on reasonable maximum transmit power, in the case the
noise power is less than the received power pilot. The maximum EE was
achieved with the desired maximum transmit power threshold by minimizing pilot reuse, in the case the transmit power allocation ρd = 40 dBm, and the
optimal EE=71.232 Mb/j
Finding Base-Station Locations in Two-Tiered Wireless Sensor Networks by Particle Swarm Optimization
In wireless sensor networks, minimizing power consumption to prolong network lifetime is very crucial. In the past, Pan et al. proposed two algorithms to find the optimal locations of base stations in two-tiered wireless sensor networks. Their approaches assumed the initial energy and the energy-consumption parameters were the same for all application nodes. If any of the above parameters were not the same, their approaches could not work. Recently, the PSO technique has been widely used in finding nearly optimal solutions for optimization problems. In this paper, an algorithm based on particle swarm optimization (PSO) is thus proposed for general power-consumption constraints. The proposed approach can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results also show the good performance of the proposed PSO approach and the effects of the parameters on the results. The proposed algorithm can thus help find good BS locations to reduce power consumption and maximize network lifetime in two-tiered wireless sensor networks. Keywords: wireless sensor network, network lifetime, energy consumption, particle swarm optimization, base station
Minimizing Base Station Power Consumption
We propose a new radio resource management algorithm which aims at minimizing
the base station supply power consumption for multi-user MIMO-OFDM. Given a
base station power model that establishes a relation between the RF transmit
power and the supply power consumption, the algorithm optimizes the trade-off
between three basic power-saving mechanisms: antenna adaptation, power control
and discontinuous transmission. The algorithm comprises two steps: a) the first
step estimates sleep mode duration, resource shares and antenna configuration
based on average channel conditions and b) the second step exploits
instantaneous channel knowledge at the transmitter for frequency selective
time-variant channels. The proposed algorithm finds the number of transmit
antennas, the RF transmission power per resource unit and spatial channel, the
number of discontinuous transmission time slots, and the multi-user resource
allocation, such that supply power consumption is minimized. Simulation results
indicate that the proposed algorithm is capable of reducing the supply power
consumption by between 25% and 40%, dependend on the system load.Comment: 27 page
Optimization of Energy Efficient Advance Leach Protocol
In WSNs, the only source to save life for the node is the battery consumption. During communication with other area nodes or sensing activities consumes a lot of power energy in processing the data and transmitting the collected/selected data to the sink. In wireless sensor networks, energy conservation is directly to the network lifetime and energy plays an important role in the cluster head selection. A new threshold has been formulated for cluster head selection, which is based on remaining energy of the sensor node and the distance from the base station. Proposed approach selects the cluster head nearer to base station having maximum remaining energy than any other sensor node in multi-hop communication. The multi hop approach minimizing the inter cluster communication without effecting the data reliability
A Metric for Secrecy-Energy Efficiency Tradeoff Evaluation in 3GPP Cellular Networks
Physical-layer security is now being considered for information protection in future wireless communications. However, a better understanding of the inherent secrecy of wireless systems under more realistic conditions, with a specific attention to the relative energy consumption costs, has to be pursued. This paper aims at proposing new analysis tools and investigating the relation between secrecy capacity and energy consumption in a 3rd Generation Partnership Project (3GPP) cellular network , by focusing on secure and energy efficient communications. New metrics that bind together the secure area in the Base Station (BS) sectors, the afforded date-rate and the power spent by the BS to obtain it, are proposed that permit evaluation of the tradeoff between these aspects. The results show that these metrics are useful in identifying the optimum transmit power level for the BS, so that the maximum secure area can be obtained while minimizing the energy consumption
Power-Aware Planning and Design for Next Generation Wireless Networks
Mobile network operators have witnessed a transition from being voice dominated to video/data domination, which leads to a dramatic traffic growth over the past decade. With the 4G wireless communication systems being deployed in the world most recently, the fifth generation (5G) mobile and wireless communica- tion technologies are emerging into research fields. The fast growing data traffic volume and dramatic expansion of network infrastructures will inevitably trigger tremendous escalation of energy consumption in wireless networks, which will re- sult in the increase of greenhouse gas emission and pose ever increasing urgency on the environmental protection and sustainable network development. Thus, energy-efficiency is one of the most important rules that 5G network planning and design should follow.
This dissertation presents power-aware planning and design for next generation wireless networks. We study network planning and design problems in both offline planning and online resource allocation. We propose approximation algo- rithms and effective heuristics for various network design scenarios, with different wireless network setups and different power saving optimization objectives. We aim to save power consumption on both base stations (BSs) and user equipments (UEs) by leveraging wireless relay placement, small cell deployment, device-to- device communications and base station consolidation.
We first study a joint signal-aware relay station placement and power alloca- tion problem with consideration for multiple related physical constraints such as channel capacity, signal to noise ratio requirement of subscribers, relay power and
network topology in multihop wireless relay networks. We present approximation schemes which first find a minimum number of relay stations, using maximum transmit power, to cover all the subscribers meeting each SNR requirement, and then ensure communications between any subscriber and a base station by ad- justing the transmit power of each relay station. In order to save power on BS, we propose a practical solution and offer a new perspective on implementing green wireless networks by embracing small cell networks. Many existing works have proposed to schedule base station into sleep to save energy. However, in reality, it is very difficult to shut down and reboot BSs frequently due to nu- merous technical issues and performance requirements. Instead of putting BSs into sleep, we tactically reduce the coverage of each base station, and strategi- cally place microcells to offload the traffic transmitted to/from BSs to save total power consumption.
In online resource allocation, we aim to save tranmit power of UEs by en- abling device-to-device (D2D) communications in OFDMA-based wireless net- works. Most existing works on D2D communications either targeted CDMA- based single-channel networks or aimed at maximizing network throughput. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize total power consumption while meeting user data rate requirements. We propose to solve it using a joint optimization approach by presenting two effective and efficient algorithms, which both jointly determine mode selection, channel allocation and power assignment.
In the last part of this dissertation, we propose to leverage load migration and base station consolidation for green communications and consider a power- efficient network planning problem in virtualized cognitive radio networks with the objective of minimizing total power consumption while meeting traffic load demand of each Mobile Virtual Network Operator (MVNO). First we present a
Mixed Integer Linear Programming (MILP) to provide optimal solutions. Then we present a general optimization framework to guide algorithm design, which solves two subproblems, channel assignment and load allocation, in sequence. In addition, we present an effective heuristic algorithm that jointly solves the two subproblems.
Numerical results are presented to confirm the theoretical analysis of our schemes, and to show strong performances of our solutions, compared to several baseline methods
Energy Consumption Optimization in Mobile Communication Networks
This work addresses the challenge of minimizing the energy consumption of a
wireless communication network by joint optimization of the base station
transmit power and the cell activity. A mixed-integer nonlinear optimization
problem is formulated, for which a computationally tractable linear inner
approximation algorithm is provided. The proposed method offers great
flexibility in optimizing the network operation by considering multiple system
parameters jointly, which mitigates a major drawback of existing
state-of-the-art schemes that are mostly based on heuristics. Simulation
results show that the proposed method exhibits high performance in decreasing
the energy consumption, and provides implicit load balancing in difficult high
demand scenarios.Comment: This work has been submitted to the IEEE for possible publication.
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