01.12.2016 | Research | Ausgabe 1/2016 Open Access

# Interference cancellation for non-orthogonal multiple access used in future wireless mobile networks

## 1 Introduction

## 2 System model

### 2.1 NOMA basics

_{ i }(i = 1, 2) at the base station (BS) is s

_{ i }, with transmission power p

_{ i }, so the transmit signal for UE

_{ i }is

_{ i }is

_{ i }is the complex channel coefficient between UE

_{ i }and the BS; n

_{ i }denotes the receiver’s Gaussian noise, including inter-cell interference. The power density of n

_{ i }is N

_{0,i }. In the NOMA downlink, decoding is in the order of the increasing channel gain normalized by the noise and inter-cell interference power, |h

_{ i }|

^{2}/N

_{0,i }. For a 2-UE case, as shown in Fig. 1, we assume that |h

_{1}|

^{ 2 }/N

_{0,1}> |h

_{2}|

^{ 2 }/N

_{0,2}, so UE

_{1}first decodes x

_{2}and deletes its component from received signal y

_{1}. And UE

_{2}decodes x

_{2}without interference cancellation, because it has the first decoding order. The throughput of UE

_{ i }, R

_{ i }is

### 2.2 NOMA-MIMO with practical SIC schemes

_{1}is a cell-center user (near user) and UE

_{2}is a cell-edge user (far user). Figure 3 shows the block diagram of transmitter and receiver for the downlink NOMA scheme. We assume there are two transmission antennas at the BS, and each antenna transmits signal to one UE. The transmit signal x

_{ i }for UE

_{ i }is

_{ i }is the allocated power, and s

_{ i }is the transmitted data for UE

_{ i }. After transmitting through a 2 × 2 channel H, as shown in Fig. 2, the 2-UE system is presented as

_{ i }(i = 1, 2) is the receiver’s Gaussian noise for UE

_{ i }. The received signal y

_{ i }for UE

_{ i }is represented as

_{ ji }denotes the channel between the jth antenna at the BS and the ith receiver, x

_{ j }is the transmit signal for UE

_{ j }, which is the interference for UE

_{ i }, and n

_{ i }the Gaussian noise. After reception, the signals are ranked in decreasing order by power. Channel estimation (CE) is performed for the interference signal y

_{ ji }with power p

_{ j }> p

_{ i }(power of the desired signal). Then, SIC is employed until all interference signals are cancelled. The estimated received signal \( {\tilde{s}}_i \) is obtained for UE

_{ i }. For the 2-UE case, from Eq. (6), the received signals for UE

_{1}and UE

_{2}are

_{2}is a far user with greater power, the element H

_{21}x

_{2}is cancelled by the IC from y

_{1}, whereas y

_{2}can be demodulated directly without IC.

_{2}>> p

_{1}, the UE

_{2}can directly detect the signal without cancellation of interference. For UE

_{1}, the received signal y

_{1}is

_{21}, i.e., Ĥ

_{21}|

_{ZF}or Ĥ

_{21}|

_{MMSE}, by ZF or MMSE criteria after getting the estimated channel \( {\tilde{H}}_{21} \):

_{1}. Then, the estimated interference signal can be obtained as

_{1}can detect the desired information, s

_{1}, from the updated received signal.

## 3 IC error analysis

### 3.1 Comparison between conventional MA and NOMA with practical IC scheme

_{1}(near user), p

_{1}, is assigned as 0.8, and for UE

_{2}(far user), p

_{2}, is 0.2, which follows the water filling (WF) algorithm. For NOMA, e.g., p

_{1}= 0.2, p

_{2}= 0.8 [4], which is power control (PC). We imitate system-level simulation (SLS) by LLS, where power allocation is used instead of path loss for cases without and with NOMA (i.e., WF versus PC). According to the LTE specifications, simulation parameters are summarized in Table 1. Figure 4 shows the PER performance for cases with NOMA (case_1) and without NOMA (case_2), where the practical ZF IC is applied for UE

_{1}in case_2. According to the PER, the throughput C can be calculated by

Parameters | Value |
---|---|

Bandwidth | 1.4 MHz |

Subcarriers per resource block | 12 |

Symbols per packet | 6 |

Resource blocks | 6 |

T
_{packet}
| 0.5 ms |

Number of bits per packet (n
_{bit/packet}) | 864 |

Channel | AWGN |

Modulation | QPSK |

Power for NOMA (case_1) |
p
_{1} = 0.2, p
_{2} = 0.8 |

Power for without NOMA (case_2) |
p
_{1} = 0.8, p
_{2} = 0.2 |

_{1}= 0.2, p

_{2}= 0.8) and case_2 (p

_{1}= 0.8, p

_{2}= 0.2) under the scenario without NOMA, the performance of UE

_{1}in case_1 is same as UE

_{2}in case_2. And the performance of UE

_{2}in case_1 is the same as UE

_{1}in case_2. So, it is fair to compare the scenario of case_1 (with NOMA) and case_2 (without NOMA).

_{2}can detect information with greater power than interference from UE

_{1}. Without NOMA, UE

_{2}cannot detect information owing to the lower power compared to the interference signal from UE

_{1}. On the other hand, UE

_{1}with NOMA can benefit from IC, even though the power of the interference signal is much higher than the target signal. Without NOMA, UE

_{1}has performance similar to UE

_{2}with NOMA, owing to the existing interference from the lower power user. As the results shown in Fig. 5, NOMA can increase the sum throughput and improve the fairness between the near and far users, compared with the situations without NOMA.

_{1}under different modulation schemes, with the bound value for UE

_{1}that is obtained from Eq. (4), mentioned above. Figure 6 shows the simulation curves for UE

_{1}with NOMA under the perfect IC for different modulation and coding schemes. The coding scheme is convolutional code; the channel environment is single-path Rayleigh fading; and other simulation parameters are the same as in Table 1. From the comparison, all the simulation curves are lower than the bound and show a higher data rate with a higher order of modulation schemes. This verifies the LLS results and the analysis for the further research.

## 4 IC error under AWGN channel

_{1}(near user), IC is applied to cancel the interference from the UE

_{2}signal, as described in Fig. 7. Under the AWGN channel, the received signal for UE

_{1}is

_{2}compared to x

_{1}, x

_{2}should be demodulated first. When demodulating x

_{2}at UE

_{1}, x

_{1}becomes the interference component:

_{ 1_AWGN}is the interference for x

_{2}plus noise, and we assume that the demodulated signal of x

_{2}at UE

_{1}is \( {\tilde{x}}_{2\left|\mathrm{U}\mathrm{E}1\right.} \). Then, the noise enhancement ratio when demodulating x

_{2}at UE

_{1}is

_{1}, and the received signal after IC for x

_{2}is

_{1}after IC. The power of the remaining interference for x

_{1}is

_{2}from perfect IC. Therefore, the bound value for UE

_{1}after practical IC of x

_{2}is

_{1}NOMA under practical IC compared with perfect IC (Fig. 6).

### 4.1 ZF IC error under single-path Rayleigh channel

_{2}signal, as described in Fig. 9. The received signal for UE

_{1}is

_{11}and H

_{21}are the channel coefficients for x

_{1}and x

_{2}, respectively. Due to the larger power of x

_{2}, x

_{2}should be demodulated first. When demodulating x

_{2}at UE

_{1}, H

_{11}x

_{1}becomes the interference. After (perfect) ZF CE for x

_{2},

_{ 1_ZF}is the interference for x

_{2}plus noise, we assume the demodulated signal of x

_{2}at UE

_{1}is \( {\tilde{x}}_{2\left|\mathrm{U}\mathrm{E}1\right.} \). Then, the noise enhancement ratio when demodulating x

_{2}at UE

_{1}is

_{21}to cancel this signal from y

_{1}. The received signal after ZF IC for x

_{2}is

_{1}after ZF IC. The power of the remaining interference for x

_{1}is

_{2}from perfect IC. Therefore, the bound value for UE

_{1}after ZF IC of x

_{2}is

_{1}NOMA with perfect and practical ZF IC under the single-path Rayleigh channel. From the figure, both bound and simulation curves show the loss due to the practical IC error, compared with perfect IC.

### 4.2 MMSE IC error under single-path Rayleigh channel

_{1}, the MMSE IC is applied to cancel the interference from the UE

_{2}signal, as described in Fig. 11. The received signal for UE

_{1}is

_{11}and H

_{21}are the channel coefficients for x

_{1}and x

_{2}, respectively. Due to the greater power of x

_{2}, x

_{2}should be demodulated first. When demodulating x

_{2}at UE

_{1}, H

_{11}x

_{1}becomes the interference. We assume perfect channel estimation, so the MMSE weight factor for channel H

_{21}is

_{2}

_{ 1_MMSE}is the interference for x

_{2}plus noise, we assume the demodulated signal of x

_{2}at UE

_{1}is \( {\tilde{x}}_{2\left|\mathrm{U}\mathrm{E}1\right.} \). Then, the noise enhancement ratio when demodulating x

_{2}at UE

_{1}is

_{21}to cancel this signal from y

_{1}. The received signal after MMSE IC for x

_{2}is

_{1}after MMSE IC. The power of the remaining interference for x

_{1}is

_{2}from perfect IC. Therefore, the bound value for UE

_{1}after MMSE IC of x

_{2}is

_{1}NOMA with practical ZF and MMSE IC under the single-path Rayleigh channel. From the simulation results, both bound and simulation curves show the gain from MMSE IC compared with ZF IC.

## 5 Proposed interference-predicted MMSE IC schemes for NOMA

### 5.1 Proposed interference-predicted MMSE IC scheme

_{2}plus noise after MMSE CE for x

_{2}is

_{1_MMSE}is the background noise for demodulating x

_{2}, so then, the IPMMSE weight factor is

_{1_MMSE}:

_{1}with conventional OMA and NOMA for different IC schemes under single-path Rayleigh fading channel. The modulation and coding scheme used in the simulation is quadrature phase-shift keying (QPSK) with 1/2 convolutional code. From the simulation results, we find that OMA has the best BER performance, because there is no interference for OMA signaling. As for NOMA, ZF IC gives the worst BER, but it has the lowest complexity. And MMSE IC shows the better BER performance than ZF IC, which is in accordance with our previous analysis in Section 3. Finally, the proposed IPMMSE IC gives the best BER performance among the NOMA IC schemes, because it considers the effect of the interference signals. When the target BER is 10

^{−3}, the IPMMSE IC scheme outperforms the ZF IC scheme by around 1 dB and by 0.2 dB over the MMSE IC scheme.

### 5.2 Proposed remaining interference-predicted MMSE IC scheme

_{2}at UE

_{1}is

_{1}is

_{2}from perfect IC. The next step is to demodulate x

_{1}, so we propose the remaining interference-predicted MMSE (RIPMMSE) IC for x

_{1}to cancel the remaining interference. The RIPMMSE weight factor for channel H

_{11}is

_{1}under conventional OMA and NOMA with different IC schemes under single-path Rayleigh fading channel. The modulation and coding scheme used in the simulation is QPSK with 1/3 convolutional code. The simulation results show that RIPMMSE IC can further improve the BER performance compared to IPMMSE IC for NOMA. When the target BER is 10

^{−3}, the RIPMMSE IC scheme can provide a 1.5-dB gain, compared to the ZF IC scheme, and a 0.5 dB gain over the MMSE IC scheme. Under the same principle, for 3-UE NOMA, the MMSE weight factor can be updated for a third time to further improve the BER performance.

## 6 Conclusions

^{−3}. In the future work, a more general case, i.e., larger number of users will be examined with our proposed schemes.