7,014 research outputs found
Analysis and Optimization of Cellular Network with Burst Traffic
In this paper, we analyze the performance of cellular networks and study the
optimal base station (BS) density to reduce the network power consumption. In
contrast to previous works with similar purpose, we consider Poisson traffic
for users' traffic model. In such situation, each BS can be viewed as M/G/1
queuing model. Based on theory of stochastic geometry, we analyze users'
signal-to-interference-plus-noise-ratio (SINR) and obtain the average
transmission time of each packet. While most of the previous works on SINR
analysis in academia considered full buffer traffic, our analysis provides a
basic framework to estimate the performance of cellular networks with burst
traffic. We find that the users' SINR depends on the average transmission
probability of BSs, which is defined by a nonlinear equation. As it is
difficult to obtain the closed-form solution, we solve this nonlinear equation
by bisection method. Besides, we formulate the optimization problem to minimize
the area power consumption. An iteration algorithm is proposed to derive the
local optimal BS density, and the numerical result shows that the proposed
algorithm can converge to the global optimal BS density. At the end, the impact
of BS density on users' SINR and average packet delay will be discussed.Comment: This paper has been withdrawn by the author due to missuse of queue
model in Section Fou
Energy Efficient Coordinated Beamforming for Multi-cell MISO Systems
In this paper, we investigate the optimal energy efficient coordinated
beamforming in multi-cell multiple-input single-output (MISO) systems with
multiple-antenna base stations (BS) and single-antenna mobile stations
(MS), where each BS sends information to its own intended MS with cooperatively
designed transmit beamforming. We assume single user detection at the MS by
treating the interference as noise. By taking into account a realistic power
model at the BS, we characterize the Pareto boundary of the achievable energy
efficiency (EE) region of the links, where the EE of each link is defined
as the achievable data rate at the MS divided by the total power consumption at
the BS. Since the EE of each link is non-cancave (which is a non-concave
function over an affine function), characterizing this boundary is difficult.
To meet this challenge, we relate this multi-cell MISO system to cognitive
radio (CR) MISO channels by applying the concept of interference temperature
(IT), and accordingly transform the EE boundary characterization problem into a
set of fractional concave programming problems. Then, we apply the fractional
concave programming technique to solve these fractional concave problems, and
correspondingly give a parametrization for the EE boundary in terms of IT
levels. Based on this characterization, we further present a decentralized
algorithm to implement the multi-cell coordinated beamforming, which is shown
by simulations to achieve the EE Pareto boundary.Comment: 6 pages, 2 figures, to be presented in IEEE GLOBECOM 201
Area Spectral Efficiency Analysis and Energy Consumption Minimization in Multi-Antenna Poisson Distributed Networks
This paper aims at answering two fundamental questions: how area spectral
efficiency (ASE) behaves with different system parameters; how to design an
energy-efficient network. Based on stochastic geometry, we obtain the
expression and a tight lower-bound for ASE of Poisson distributed networks
considering multi-user MIMO (MU-MIMO) transmission. With the help of the
lower-bound, some interesting results are observed. These results are validated
via numerical results for the original expression. We find that ASE can be
viewed as a concave function with respect to the number of antennas and active
users. For the purpose of maximizing ASE, we demonstrate that the optimal
number of active users is a fixed portion of the number of antennas. With
optimal number of active users, we observe that ASE increases linearly with the
number of antennas. Another work of this paper is joint optimization of the
base station (BS) density, the number of antennas and active users to minimize
the network energy consumption. It is discovered that the optimal combination
of the number of antennas and active users is the solution that maximizes the
energy-efficiency. Besides the optimal algorithm, we propose a suboptimal
algorithm to reduce the computational complexity, which can achieve near
optimal performance.Comment: Submitted to IEEE Transactions on Wireless Communications, Major
Revisio
An Energy Efficient Semi-static Power Control and Link Adaptation Scheme in UMTS HSDPA
High speed downlink packet access (HSDPA) has been successfully applied in
commercial systems and improves user experience significantly. However, it
incurs substantial energy consumption. In this paper, we address this issue by
proposing a novel energy efficient semi-static power control and link
adaptation scheme in HSDPA. Through estimating the EE under different
modulation and coding schemes (MCSs) and corresponding transmit power, the
proposed scheme can determine the most energy efficient MCS level and transmit
power at the Node B. And then the Node B configure the optimal MCS level and
transmit power. In order to decrease the signaling overhead caused by the
configuration, a dual trigger mechanism is employed. After that, we extend the
proposed scheme to the multiple input multiple output (MIMO) scenarios.
Simulation results confirm the significant EE improvement of our proposed
scheme. Finally, we give a discussion on the potential EE gain and challenge of
the energy efficient mode switching between single input multiple output (SIMO)
and MIMO configuration in HSDPA.Comment: 9 pages, 11 figures, accepted in EURASIP Journal on Wireless
Communications and Networking, special issue on Green Radi
Designing Approximate Computing Circuits with Scalable and Systematic Data-Driven Techniques
Semiconductor feature size has been shrinking significantly in the past decades. This decreasing trend of feature size leads to faster processing speed as well as lower area and power consumption. Among these attributes, power consumption has emerged as the primary concern in the design of integrated circuits in recent years due to the rapid increasing demand of energy efficient Internet of Things (IoT) devices. As a result, low power design approaches for digital circuits have become of great attractive in the past few years. To this end, approximate computing in hardware design has emerged as a promising design technique. It provides design opportunities to improve timing and energy efficiency by relaxing computing quality. This technique is feasible because of the error-resiliency of many emerging resource-hungry computational applications such as multimedia processing and machine learning. Thus, it is reasonable to utilize this characteristic to trade an acceptable amount of computing quality for energy saving.
In the literature, most prior works on approximate circuit design focus on using manual design strategies to redesign fundamental computational blocks such as adders and multipliers. However, the manual design techniques are not suitable for system level hardware due to much higher design complexity. In order to tackle this challenge, we focus on designing scalable, systematic and general design methodologies that are applicable on any circuits. In this paper, we present two novel approximate circuit design methods based on machine learning techniques. Both methods skip the complicated manual analysis steps and primarily look at the given input-error pattern to generate approximate circuits. Our first work presents a framework for designing compensation block, an essential component in many approximate circuits, based on feature selection. Our second work further extends and optimizes this framework and integrates data-driven consideration into the design. Several case studies on fixed-width multipliers and other approximate circuits are presented to demonstrate the effectiveness of the proposed design methods. The experimental results show that both of the proposed methods are able to automatically and efficiently design low-error approximate circuits
Capacity of UAV-Enabled Multicast Channel: Joint Trajectory Design and Power Allocation
This paper studies an unmanned aerial vehicle (UAV)-enabled multicast
channel, in which a UAV serves as a mobile transmitter to deliver common
information to a set of ground users. We aim to characterize the capacity
of this channel over a finite UAV communication period, subject to its maximum
speed constraint and an average transmit power constraint. To achieve the
capacity, the UAV should use a sufficiently long code that spans over its whole
communication period. Accordingly, the multicast channel capacity is achieved
via maximizing the minimum achievable time-averaged rates of the users, by
jointly optimizing the UAV's trajectory and transmit power allocation over
time. However, this problem is non-convex and difficult to be solved optimally.
To tackle this problem, we first consider a relaxed problem by ignoring the
maximum UAV speed constraint, and obtain its globally optimal solution via the
Lagrange dual method. The optimal solution reveals that the UAV should hover
above a finite number of ground locations, with the optimal hovering duration
and transmit power at each location. Next, based on such a
multi-location-hovering solution, we present a successive hover-and-fly
trajectory design and obtain the corresponding optimal transmit power
allocation for the case with the maximum UAV speed constraint. Numerical
results show that our proposed joint UAV trajectory and transmit power
optimization significantly improves the achievable rate of the UAV-enabled
multicast channel, and also greatly outperforms the conventional multicast
channel with a fixed-location transmitter.Comment: To appear in the IEEE International Conference on Communications
(ICC), 201
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