We propose a novel precoding algorithm that is a zero-forcing (ZF) method combined with adaptive beamforming in the Worldwide Interoperability for Microwave Access (WiMAX) system. In a Multiuser Multiple-Input Multiple-Output (MU-MIMO) system, ZF is used to eliminate the Multiple Access Interference (MAI) in order to allow several users to share a common resource. The adaptive beamforming algorithm is used to achieve the desired SNR gain. The experimental system consists of a WiMAX base station that has 2 MIMO elements, each of which is composed of three-array antennas and two mobile terminals, each of which has a single antenna. Through computer simulations, we verified that the proposed method outperforms the conventional ZF method by at least 2.4 dB when the BER is 0.1%, or 1.7 dB when the FER is 1%, in terms of the SNR. Through a hardware implementation of the proposed method, we verified the feasibility of the proposed method for realizing a practical WiMAX base station to utilize the channel resources as efficiently as possible.
MU-MIMO technology [
To realize an MU-MIMO system, the problem of Multiple Access Interference (MAI) caused by sharing a common resource by multiple mobile terminals must be resolved. In order to remove the MAI, a signal vector to be transmitted from the base station is precoded. In this paper, we adopt a zero-forcing (ZF) precoder to remove the MAI that employs an inversion of the channel matrix as a weight of the transmitting signal vector [
To realize a beamforming system, the phase to be applied to each antenna element must be determined in such a way that the signal transmitted to the target mobile terminal becomes in-phase when it arrives at the target terminal. In order to control the phase of each transmitted signal as described earlier, the Direction of Arrival (DOA) of the target user must be known to the base station. However, the DOA is not generally known. Based on the previous conditions, we adopted the Ordinary Lagrange Beamforming (OLB) algorithm to estimate the DOA for the target user(s) [
We implemented the precoding algorithm combined with the OLB on a WiMAX system. The base station consists of two MIMO elements, each of which is composed of a three-element beamforming array. Through computer simulations, we verify that the proposed method outperforms conventional ZF by about 2.4 dB when the BER is 0.1%, or by 1.7 dB when the FER is 1%. Furthermore, the proposed method outperforms conventional ZF even further when the SNR becomes lower, because the proposed technique reduces the channel estimation error.
This paper is organized as follows. Section
Figure
System model of MU-MIMO with adaptive beamforming.
In (
In this paper, we implement the procedure of the beamforming MU-MIMO system. We selected a WiMAX system for our implementation [
Parameters for implementation.
Parameters | Base station | User |
---|---|---|
Number of antennas | 3 * 2 | 1 |
Precoding | ZF | |
Beamforming | OLB algorithm | |
Waveform | WiMAX | |
FEC | Convolutional Turbo Coding, |
|
Frame duration (DL/UL) | 3.1104 ms/1.728 ms (for 5 ms) | |
TTG/RTG | 121.2 |
|
FFT size | 1024 | |
Number of symbols (DL/UL) | 27/15 | |
Modulation scheme | 16 QAM | |
|
2.3 GHz |
The implementation of the WiMAX system combined with the proposed scheme, which is shown in Figure
Required procedure for realizing the proposed system.
Uplink
Downlink
Arrangement of pilot subcarrier in uplink frame.
As shown in Figure
Block diagram of beamforming system.
For the target users of the MU-MIMO system who share the frequency resource (i.e., subcarrier) of the common transmit data, the weight vector should be computed in such a way that the main lobe is provided in the direction of each of the target users sharing the common resource. Summing the weight vectors of all target user would be one way of achieving that purpose [
Channel estimation can be performed by using the pilot signals, the values of which are known at the receiver. In this paper, the channel estimation is first performed along the time axis and later along the frequency axis. Figure
Procedure of channel estimation for user #1.
As shown in Figure
The precoder for the uplink system is designed based on the CSI estimation described in Section
Since each antenna element and its associated path must exhibit a different phase characteristic from the others, even if the same signal is applied to the feeding port of each transmit antenna, each transmitted signal must exhibit a different phase characteristic. Calibration is the procedure of compensating for the phase differences between each of the antenna elements and the associated antenna paths. Therefore, if we want to reuse the weight vector derived in the uplink procedure in the downlink procedure, calibration is needed. Figure
Array antenna system with added calibration antenna.
Figure
Overall system architecture of implemented MU-MIMO test-bed.
As shown in Figure
Figure
Implemented CHU.
The allocation of the functions on the devices and the complexity of each function are described in the next section.
Figure
Implemented MU-MIMO test-bed.
This section provides a performance analysis of the proposed technique, including the precoder and beamformer.
The complexity of the signal processing units is shown in Table
Complexity of signal processing unit (all the digital signal processing is performed with 12 bits).
Device | Functions | Complexity (K gates) | |
---|---|---|---|
Uplink | FPGA | Ranging code Correlator | 21.9 |
FFT | 338.2 | ||
CP removal | 8.3 | ||
Timing synchronization | 597.9 | ||
Frequency synchronization | 614.3 | ||
DSP-1 | Delay estimation | — | |
Ranging code detection | — | ||
Weight calculation | — | ||
DSP-2 | Channel estimation | — | |
MIMO detection | — | ||
DSP-3 | Demodulation | — | |
FEC decoding | — | ||
Deinterleaving | |||
| |||
Total logic gates | 1580.6 | ||
| |||
Downlink | FPGA | Weight multiplication | 376.8 |
IFFT | 638.2 | ||
Permutation | 58.9 | ||
CP addition | 11.4 | ||
Preamble | 14.2 | ||
DSP-1 | Calibration | — | |
DSP-2 | MIMO encoding | — | |
DSP-3 | Modulation | — | |
Concatenation | — | ||
DSP-4 | Randomization | — | |
DSP-5 | FEC | — | |
Interleaving | — | ||
| |||
Total logic gates | 1099.5 |
Table
We show the performance of the OLB-based beamformer through computer simulations. In this paper, we used “MATLAB” provided by “MathWorks” in order to implement the simulation [
The experimental environment consists of a single base station and two mobile terminals. The base station is composed of an MU-MIMO system consisting of two MIMO antenna elements, each of which is a three-element beamforming array, while each mobile terminal is equipped with a single antenna element. We assumed that the received power of each terminal is the same. We also assumed that the signal is propagated along the line of sight path. We generated the OLB-based beamformer in accordance with equations in Table
Parameters for OLB-based beamformer simulation.
Parameters | Value |
---|---|
DOA | 0°, 60° |
|
5 dB |
|
0.99 |
|
0.01 |
Figure
Beam pattern provided by a three-element array (two users located at
As shown in Figure
Figure
BER of MU-MIMO system with conventional ZF only and ZF with beamforming.
From Figure
This section demonstrates the Frame Error Rate (FER) performance of the implemented system. Figure
Measured FER performance.
From Figure
In this paper, we proposed the ZF method combined with beamforming. The proposed technique was implemented for experimental tests of WiMAX waveforms. The experimental environment includes a single base station and two users, while the base station employs the proposed technique of ZF-based precoding (for two MIMO elements) and OLB-based beamforming (for three-element beamforming for each of the two MIMO elements), while each user is equipped with a single antenna.
We demonstrated that the proposed algorithm provides nearly twice the throughput compared to the Single-Input Single-Output (SISO) system, as well as twice the power gain compared to the conventional MU-MIMO system with precoding only. We also verified the feasibility of the proposed precoding method for real-time processing of a WiMAX base station system. The proposed method seems to be a good solution for increasing the throughput of a given frequency band.
This work was supported by the ICT Standardization program of The Ministry of Knowledge Economy (MKE).