Link tải luận văn miễn phí cho ae Kết Nối
Abstract—Energy efficiency (EE) is an important objective in
uplink wireless communications due to the limited capacity of
batteries in user equipment (UE). In this letter, we propose
a scheduling metric to balance the tradeoff between EE and
fairness. We consider the scenario in which the access point (AP)
serves a subset of UEs when available resources cannot support
all of the UEs’ quality-of-service (QoS) requirements. Based on
the scheduling metric, we propose a suboptimal joint scheduling
and resource allocation algorithm that maximizes the system EE.
Simulation results show that the proposed algorithm can achieve
a good tradeoff between the system EE and fairness.
Index Terms—Energy efficiency, resource allocation, scheduling, orthogonal frequency division multiple access (OFDMA).
I. INTRODUCTION
WITH the growing popularity of green communications, EE is becoming increasingly important [1]. The booming demand for wireless applications and the relatively low
battery capacity in UE puts a premium on achieving high EE.
Previous work on energy-efficient resource allocation in OFDMA systems has mainly focused on downlink scenarios [2].
In general, uplink energy-efficient resource allocation is less
tractable due to constraints on individual UEs and the discrete
nature of subchannel assignment. In [3], link adaptation and
resource allocation schemes for uplink OFDMA systems have
been designed using a time-averaged bit-per-Joule metric to
maximize the arithmetic average of the EE of all users. In
[4], an uplink allocation algorithm is described to maximize
the minimum EE among all users. In [5], efficient suboptimal
algorithms are proposed to maximize the total EE of all UEs
under individual power and rate constraints for the UEs. The
work in [3]–[5] assumes that an outage occurs when the QoS
constraints cannot be satisfied.
In this letter, we address the joint scheduling and resource
allocation problem, considering the EE in the uplink of multiuser OFDMA networks. We address a practical scenario: when
the limited resources cannot support all the QoS requirements
of the UEs, the AP rejects some requests and guarantees
service to the remaining UEs. Most previous scheduling algorithms are designed based on spectral efficiency (SE) [6]; we
aim at improving the system EE. In this work, we formulate
H. Ye and Z. Tan are with the Institute of Broadband Wireless Mobile
Communications, School of Electronic and Information Engineering, Beijing
Jiaotong University, Beijing 100044, China (e-mail: [email protected]).
G. Lim is with the Availink Inc., Germantown, MD, USA.
L. J. Cimini is with the Department of Electrical and Computer Engineering,
University of Delaware, Newark, DE 19716, USA.
The work of H. Ye and Z. Tan was partially supported by the NSFC under
Grant No. 61471030. The work of G. Lim and L. J. Cimini was partially by
NSF under Grant No.1017053.
a system EE maximization problem with limits on the total
power of a UE and with QoS requirements. The EE of a
UE is described as the achieved throughput per unit energy
consumption. We define the system EE as the arithmetic
average of the EE of all UEs, and the QoS constraint as the
required minimum data rate. We propose a scheduling metric
to determine the subset of active UEs, considering EE and
fairness. Then, we present a joint energy-efficient scheduling
and resource allocation algorithm to address the problem with
an acceptable complexity.
II. SYSTEM MODEL AND PROBLEM FORMULATION
We consider an uplink OFDMA system where a set of
UEs, K = f1; : : : ; Kg, attempts to access a single AP. The
bandwidth B is divided into a set of subchannels, N =
f1; : : : ; Ng, each with bandwidth W = B=N. Based on
the channel state information (CSI) of all the UEs, the AP
assigns subchannels and powers. Let pk;n denote the transmit
power on subchannel n for UE k, and hk;n the corresponding channel coefficient, modelled as a zero-mean complex
Gaussian random variable with a variance that incorporates
the distance-based path-loss component. Then, the maximum
achievable data rate, rk;n, of the kth UE on the nth subchannel
is rk;n = W log2 (1 + pk;ngk;n), where gk;n , jhNk;n oWj2 , and
No is the one-sided noise power spectral density.
We define the EE of the kth UE as the delivered bits per
unit energy (in bits/Joule)
EEk = Rk
Pk
=
∑N n=1 ρk;nrk;n
ζ ∑N n=1 ρk;npk;n + Pk;c ; (1)
where Rk denotes the data rate of the kth UE, and ρk;n 2
f1; 0g indicates whether or not subchannel n is assigned to
UE k. The power consumption Pk consists of the power
consumed in transmission plus the circuit power consumption
Pk;c. The multiplier ζ represents the inverse of the power
amplifier efficiency; we assume ζ is constant and the same
for all UEs. The resource allocation problem that maximizes
the EE of the overall network is
max
pk;n;ρk;n [EE = k∑K=1 EEk = k∑K=1 ζ ∑N n∑=1N n=1 ρk;n ρk;n pk;n rk;n + Pk;c ] ;
(2a)
subject to
N∑ n
=1
ρk;npk;n ≤ Pk;max; 8k 2 K; (2b)
Do Drive thay đổi chính sách, nên một số link cũ yêu cầu duyệt download. các bạn chỉ cần làm theo hướng dẫn.
Password giải nén nếu cần: ket-noi.com | Bấm trực tiếp vào Link để tải:
Abstract—Energy efficiency (EE) is an important objective in
uplink wireless communications due to the limited capacity of
batteries in user equipment (UE). In this letter, we propose
a scheduling metric to balance the tradeoff between EE and
fairness. We consider the scenario in which the access point (AP)
serves a subset of UEs when available resources cannot support
all of the UEs’ quality-of-service (QoS) requirements. Based on
the scheduling metric, we propose a suboptimal joint scheduling
and resource allocation algorithm that maximizes the system EE.
Simulation results show that the proposed algorithm can achieve
a good tradeoff between the system EE and fairness.
Index Terms—Energy efficiency, resource allocation, scheduling, orthogonal frequency division multiple access (OFDMA).
I. INTRODUCTION
WITH the growing popularity of green communications, EE is becoming increasingly important [1]. The booming demand for wireless applications and the relatively low
battery capacity in UE puts a premium on achieving high EE.
Previous work on energy-efficient resource allocation in OFDMA systems has mainly focused on downlink scenarios [2].
In general, uplink energy-efficient resource allocation is less
tractable due to constraints on individual UEs and the discrete
nature of subchannel assignment. In [3], link adaptation and
resource allocation schemes for uplink OFDMA systems have
been designed using a time-averaged bit-per-Joule metric to
maximize the arithmetic average of the EE of all users. In
[4], an uplink allocation algorithm is described to maximize
the minimum EE among all users. In [5], efficient suboptimal
algorithms are proposed to maximize the total EE of all UEs
under individual power and rate constraints for the UEs. The
work in [3]–[5] assumes that an outage occurs when the QoS
constraints cannot be satisfied.
In this letter, we address the joint scheduling and resource
allocation problem, considering the EE in the uplink of multiuser OFDMA networks. We address a practical scenario: when
the limited resources cannot support all the QoS requirements
of the UEs, the AP rejects some requests and guarantees
service to the remaining UEs. Most previous scheduling algorithms are designed based on spectral efficiency (SE) [6]; we
aim at improving the system EE. In this work, we formulate
H. Ye and Z. Tan are with the Institute of Broadband Wireless Mobile
Communications, School of Electronic and Information Engineering, Beijing
Jiaotong University, Beijing 100044, China (e-mail: [email protected]).
G. Lim is with the Availink Inc., Germantown, MD, USA.
L. J. Cimini is with the Department of Electrical and Computer Engineering,
University of Delaware, Newark, DE 19716, USA.
The work of H. Ye and Z. Tan was partially supported by the NSFC under
Grant No. 61471030. The work of G. Lim and L. J. Cimini was partially by
NSF under Grant No.1017053.
a system EE maximization problem with limits on the total
power of a UE and with QoS requirements. The EE of a
UE is described as the achieved throughput per unit energy
consumption. We define the system EE as the arithmetic
average of the EE of all UEs, and the QoS constraint as the
required minimum data rate. We propose a scheduling metric
to determine the subset of active UEs, considering EE and
fairness. Then, we present a joint energy-efficient scheduling
and resource allocation algorithm to address the problem with
an acceptable complexity.
II. SYSTEM MODEL AND PROBLEM FORMULATION
We consider an uplink OFDMA system where a set of
UEs, K = f1; : : : ; Kg, attempts to access a single AP. The
bandwidth B is divided into a set of subchannels, N =
f1; : : : ; Ng, each with bandwidth W = B=N. Based on
the channel state information (CSI) of all the UEs, the AP
assigns subchannels and powers. Let pk;n denote the transmit
power on subchannel n for UE k, and hk;n the corresponding channel coefficient, modelled as a zero-mean complex
Gaussian random variable with a variance that incorporates
the distance-based path-loss component. Then, the maximum
achievable data rate, rk;n, of the kth UE on the nth subchannel
is rk;n = W log2 (1 + pk;ngk;n), where gk;n , jhNk;n oWj2 , and
No is the one-sided noise power spectral density.
We define the EE of the kth UE as the delivered bits per
unit energy (in bits/Joule)
EEk = Rk
Pk
=
∑N n=1 ρk;nrk;n
ζ ∑N n=1 ρk;npk;n + Pk;c ; (1)
where Rk denotes the data rate of the kth UE, and ρk;n 2
f1; 0g indicates whether or not subchannel n is assigned to
UE k. The power consumption Pk consists of the power
consumed in transmission plus the circuit power consumption
Pk;c. The multiplier ζ represents the inverse of the power
amplifier efficiency; we assume ζ is constant and the same
for all UEs. The resource allocation problem that maximizes
the EE of the overall network is
max
pk;n;ρk;n [EE = k∑K=1 EEk = k∑K=1 ζ ∑N n∑=1N n=1 ρk;n ρk;n pk;n rk;n + Pk;c ] ;
(2a)
subject to
N∑ n
=1
ρk;npk;n ≤ Pk;max; 8k 2 K; (2b)
Do Drive thay đổi chính sách, nên một số link cũ yêu cầu duyệt download. các bạn chỉ cần làm theo hướng dẫn.
Password giải nén nếu cần: ket-noi.com | Bấm trực tiếp vào Link để tải:
You must be registered for see links