The efficiency of adaptive modulation and coding (AMC) procedure in high speed Downlink packet access (HSDPA) depends on the frequency of the channel quality information (CQI) reports transmitted by the UE to Node B. The more frequent the reports are the more accurate the link adaptation procedure is. On the other hand, the frequent CQI reports increase uplink interference, reducing thus the signal reception quality at the uplink. In this study, we propose an improved CQI reporting scheme which aims to reduce the required CQI signaling by exploiting a CQI prediction method based on a finite-state Markov chain (FSMC) model of the wireless channel. The simulation results show that under a high downlink traffic load, the proposed scheme has a near-to-optimum performance while produces less interference compared to the respective periodic CQI scheme.
High-speed packet access (HSPA) aims to advance the performance of the existing UMTS networks by improving the level of quality of service (QoS) and increasing the supported peak data rates at both the forward and the reverse link. HSPA consists of the HSDPA and high-speed uplink packet access (HSUPA) standards.
AMC procedure of HSDPA makes possible the
adaptation of the employed transport format and resource combination (TFRC) to
the wireless channel variations as well as to the varying rate requirements of
the UE. However, as the rate of the wireless channel variation increases, more frequent
CQI reports by the UE to Node B are needed in order to have an efficient AMC.
On the other hand, as the number of CQI reports increases, uplink interference
also increases, thus reducing uplink reception quality. The authors of [
The remainder of the paper is organized as
follows: in Section
The HSDPA operation utilizes a number of new channels [
The user data are transmitted through the high-speed Downlink shared channel (HS-DSCH) while the associated signaling is transmitted through the high-speed shared control channel (HS-SCCH) at the forward link and at the high-speed dedicated physical control channel (HS-DPCCH) in the uplink.
The feedback information from the terminal
to the base station, carried on the HS-DPCCH, is essential for the HSDPA
operation as it makes possible the use of adaptive modulation and coding. The
HS-DPCCH frame consists of three slots and has duration of 2 milliseconds. The
first slot is used by the hybrid automatic repeat request (HARQ) process, while
the other two slots are used for the transmission of CQI. CQI is not a direct signal-to-interference and noise ratio (SINR) measurement but instead it is an integer
index to the TFRC which the UE requests from the packet scheduler located at Node B
[
Given a typical target error rate of 10%,
the requested TFRC corresponds to the maximum transport block size which has a
minimum of 90% probability to be transmitted correctly. Two successive CQI
values correspond approximately to a step of 1 dB at the SINR of the HS-DSCH [
At Release 5, 3GPP [
CQI reporting schemes.
It is obvious that
the shorter the report cycle is, the more advantageous for the AMC procedure of
HSDPA it is, as it provides
better adaptation to the variations of the wireless channel. However, at the
same time, the frequent CQI signaling increases uplink interference and thus
decreases the average UE throughput as well as the achievable
energy-per-bit
to noise
In this paper, we propose an improved CQI reporting scheme which aims to
reduce the required CQI signaling even when the downlink data activity is high
by employing a CQI prediction method. According to this
scheme, Node B predicts all the
intermediary CQI reports between two subsequent CQI
reports by utilizing an FSMC model of the wireless
channel. Thus, as shown in Figure
The proposed prediction-based CQI reporting scheme (P-CQI) is based on
the optimum periodic-CQI feedback scheme [
Adopting the simplified interference
analysis presented at [
Ratio of predicted CQI | 1/2 | 2/3 | 3/4 | 4/5 |
---|---|---|---|---|
reports/total CQI reports | ||||
1,48 | 2,23 | 2,67 | 2,98 |
As we can see in Table
The CQI reports indicate the requested transport format by the UE and thus they reflect the current channel conditions. Therefore, they can be interpreted to SINR measurements. The obtained SINR values are then used by the P-CQI scheme to predict, through an FSMC model, the next state of the wireless channel and therefore the next CQI. Consequently, we can predict the next CQI at Node B reducing thus the number of the needed CQI reports.
Let
Assuming that the channel fades
slowly with respect to the CQI feedback
report cycle and the Doppler shift
The CQI prediction is based on the constructed FSMC wireless channel model. Given
the current state of the channel which is computed by a CQI report, the next
state
More future states may be predicted in the same manner using the same
calculated transition probabilities. The SINR value is then translated at Node B to the respective CQI value (i.e.,
TFRC). The state transition probabilities are updated periodically based on the
real SINR levels received from the mobiles each time the real CQI measurements
are collected. The steps involved in the prediction procedure are illustrated
in Figure
CQI prediction method.
For the packet scheduling, we employ the
scheduler presented at [
According to this scheme, a priority
In HSDPA, the transmitted signal is modulated with either QPSK or 16QAM. The
The flows are sorted in decreasing order of their priorities and the scheduler assigns, to the highest priority connection, the maximum TFRC for the current subframe according to the requested (or predicted) CQI and the available HSDPA capacity. The rate allocation procedure continues with the next connection in the sorted list until either (a) all the connections of the list are examined and all their respective queued packets are scheduled for transmission during the next subframe, or (b) all the available capacity has been allocated.
The performance of the proposed P-CQI reporting scheme is evaluated via event-driven simulation. We consider a cell with a radius of 1 km. Node B is located at the centre of the cell. The session arrival process is modelled by a Poisson distribution, while the session duration is exponentially distributed with equal mean. The traffic load increases by increasing the number of users in the cell. At the downlink, the user’s data are transmitted through HSDPA while on the uplink the HS-DPCCH is employed for the transmission of the signaling data.
For each connection the traffic is assumed to arrive according to an “ON-OFF” model
during the duration of the session. As long as the
connection is in the “OFF” state, it has no arrivals. While in the “ON” state, a batch of
The initial
location of each UE is randomly distributed in the cell, the directions of
movement of the users are uniformly distributed, while their velocity is also uniformly
distributed in a [0,3] km/h interval. The macrocell propagation model proposed
in [
If the CQI information is not accurate, the AMC operation of HS-DSCH cannot be efficient. As a consequence, this affects various metrics, such as bit-error rate (BER), packet delay and throughput. In the following, we evaluate the proposed P-CQI scheme by measuring the effect of prediction on the above metrics.
Considering the worst case, where E-CQI performs as the
periodic-CQI due to high downlink activity, we assume
services with
As we can see in Figure
Average bit-error rate under periodic-CQI and P-CQI.
This conclusion can also be
verified by Figures
Average packet delay under periodic-CQI and P-CQI.
Average cell throughput under periodic-CQI and P-CQI.
In this paper, we propose a prediction-based CQI reporting scheme (P-CQI). P-CQI has better performance compared with a periodic-CQI scheme which causes the same uplink interference. Due to the prediction of a number of CQI reports, the P-CQI scheme reduces significantly the required CQI signaling while at the same time has a performance near to optimum. As a consequence, the reception quality at the uplink is increased. The only induced cost by the use of P-CQI is an insignificant increase of the complexity at Node B because Node B has to calculate a number of CQI reports instead of receiving them from the UE.