Urban macro-cell scenario

Transcription

1 Urban macro-cell scenario In typical urban macro-cell scenario [Kyösti2007, Section 2.3.9], user equipment is located outdoors at street level and fixed base station clearly above surrounding building heights. The building blocks can form either a regular Manhattan type of grid, or have more irregular locations. Typical building heights in urban environments are over four floors. Buildings height and density in typical urban macro-cell are mostly homogenous. In Figure 112 and Figure 113, we plot the average bit errors obtained for LTE signal in urban micro-cell scenario where Alice, Bob, and Eve are equipped with single antenna. QPSK modulation is used with 1/3 coding rate. The biterror-rate performance in the uplink direction is better, compared to downlink direction, because in the uplink direction the base station uses much better MMSE detector that is capable of suppressing self-interference caused by tag signals. In the downlink direction, least-squares-based detector is used. This selection of different detectors reflects the constraints and limitations on computational complexity of devices; base stations are more capable than user equipment. In general, the results as well as observations are similar as in the indoor office scenario. The presence of tag signal impairs detection capabilities of LTE detectors because the tag signal is seen as additional noise component. As expected, the larger the value of tag-to-signal ratio, the LTE detector performs worse. However, in some cases and scenarios, the turbo-decoder is able to compensate for the presence of tag-signal. In Figure 114 and Figure 115 we plot the probabilities of detecting the tag signal obtained by Bob and Eve. The simulation results suggest that there is no significant difference in tag detection probabilities in uplink and downlink direction. Furthermore, the results demonstrate that the power level of tag signal of -30 db below LTE signal is way too small to guarantee reliable detection of tag signal. However, it can be seen that Bob is able to detect the presence of the tag signals with high probability when signal-to-noise ratio exceeds 10 db provided that the tag-to-signal-ratio equals 10 db. On the other hand, Eve is not able to detect reliably the presence of the tag signals. Namely, Eve s probability of detection is below 8 per cent provided that tag-to-signal ratio equals 10 db. The Eve s probability of detection at level up to 8 per cent is to be expected because there are 127 Kasami sequences of order 14. Eve guesses the correct signal with probability 1/127 which is approximately 8 times in 100 trials. In Figure 116 and Figure 117 we plot the mean value of the channel estimation error obtained by Bob and Eve. It can be seen that Bob is able to estimate the channel whereas Eve is not able to accurately estimate the channel, mainly because she does not know the tag signal. She tries to guess it or she may try to apply one by one to find the right one. It can be seen that the accuracy of the estimation improves with the power level of tag signal. In general, the trend in the LTE simulation results is similar as those computed for WiFi, see the results in Deliverable 4.1 Figures 25 to 30. The performance of channel estimation in LTE environment is slightly worse than in the WiFi environment. More specifically, the LTE channel estimation error is bigger than the corresponding error in WiFi as shown in Deliverable 4.1. We speculate that the main reason for it is the fact that in LTE the channel is estimated over longer periods: L=144 taps versus L=16 taps, which are, respectively, the length of cyclic prefix for LTE and WiFi. In Figure 118 to Figure 123 we plot bit error rate performance, tag detection probability, and mean channel estimation error for single antenna transmission with 16-QAM modulation format and coding rate ½. Similarly, in Figure 124 to Figure 129, we plot bit error rate performance, tag detection probability, and mean channel estimation error for single antenna transmission with 64-QAM modulation format and coding rate 3/4. In general, the results follow those obtained for indoor office and urban micro-cell scenarios and are not repeated here in full details. With high-order modulations, there are no significant differences in tag signal detection probability and channel estimation errors compared to QPSK case. Higher-order modulations are more sensitive to self-interference caused by tag signals. More specifically, whereas 16-QAM modulation can perform reasonably well at tag-to-signal ratio of -10 db, for 64-QAM modulation the such level of self-interference is detrimental. For 64-QAM modulation, tagsignal at power level 20 db below LTE signal seems to be adequate. We plot the respective bit error rates, tag detection probabilities, and mean estimation errors in Figure 130 to Figure 165 for multi-antenna configurations. In general, the main observations follow those obtained for indoor office scenario and urban micro-cell scenarios and thus are not repeated here in full details. The results for multiple antenna scenarios are similar to those obtained for single antenna scenario. This is a simple consequence of the fact that each transmit antenna sends a unique tag signal which form a set of quasi-orthogonal signals. The bit error rate performance in multi-antenna channels are significantly improved compared to single antenna scenario because we use transmit diversity scheme page 81 /

2 Single-input single-output antenna system (TS-C2-1) Figure 112: Bit-error-rate of Bob in downlink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4). Bit error rate Bit error rate Figure 113: Bit-error-rate of Bob in uplink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4) page 82 /

3 Figure 114: Tag-detection probability in downlink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4). Detection probability Detection probability Figure 115: Tag-detection probability in uplink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4) page 83 /

4 Figure 116: in downlink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4). E[ ] E[ ] Figure 117: in uplink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4) page 84 /

5 Figure 118: Bit-error-rate of Bob in downlink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8). Bit error rate Bit error rate Figure 119: Bit-error-rate of Bob in uplink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8) page 85 /

6 Figure 120: Tag-detection probability in downlink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8). Detection probability Detection probability Figure 121: Tag-detection probability in uplink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8) page 86 /

7 Figure 122: in downlink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8). E[ ] E[ ] Figure 123: in uplink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8) page 87 /

8 Figure 124: Bit-error-rate of Bob in downlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13). Bit error rate Bit error rate Figure 125: Bit-error-rate of Bob in uplink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13) page 88 /

9 Figure 126: Tag-detection probability in downlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13). Detection probability Detection probability Figure 127: Tag-detection probability in uplink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13) page 89 /

10 Figure 128: in downlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13). E[ ] E[ ] Figure 129: in uplink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13) page 90 /

11 Multiple-input multiple-output antenna system (TS-C2-2) Figure 130: Bit error rate in MIMO 2x2 downlink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4). Bit-error-rate Bit-error-rate Figure 131: Bit error rate in MIMO 2x2 uplink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4) page 91 /

12 Figure 132: Tag detection probability in MIMO 2x2 downlink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4). Figure 133: Tag detection probability in MIMO 2x2 uplink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4) page 92 /

13 Figure 134: in MIMO 2x2 downlink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4). Figure 135: in MIMO 2x2 uplink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4) page 93 /

14 Figure 136: Bit error rate in MIMO 2x2 downlink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8). Bit-error-rate Bit-error-rate Figure 137: Bit error rate in MIMO 2x2 uplink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8) page 94 /

15 Figure 138: Tag detection probability in MIMO 2x2 downlink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8). Figure 139: Tag detection probability in MIMO 2x2 uplink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8) page 95 /

16 Figure 140: in MIMO 2x2 downlink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8). Figure 141: in MIMO 2x2 uplink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8) page 96 /

17 Figure 142: Bit error rate in MIMO 2x2 downlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13). Bit-error-rate Bit-error-rate Figure 143: Bit error rate in MIMO 2x2 uplink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13) page 97 /

18 Figure 144: Tag detection probability in MIMO 2x2 downlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13). Figure 145: Tag detection probability in MIMO 2x2 uplink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13) page 98 /

19 Figure 146: in MIMO 2x2 downlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13). Figure 147: in MIMO 2x2 uplink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13) page 99 /

20 Multiple-input multiple-output antenna system (TS-C2-3) Figure 148: Bit error rate in MIMO 4x2 downlink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4). Bit-error-rate Bit-error-rate Figure 149: Bit error rate in MIMO 4x2 uplink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4) page 100 /

21 Figure 150: Tag detection probability in MIMO 4x2 downlink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4). Figure 151: Tag detection probability in MIMO 4x2 uplink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4) page 101 /

22 Figure 152: in MIMO 4x2 downlink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4). Figure 153: in MIMO 4x2 uplink urban macro-cell environment with QPSK modulation and 1/3 coding rate (CQI=4) page 102 /

23 Figure 154: Bit error rate of Bob in MIMO 4x2 downlink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8). Bit-error-rate Bit-error-rate Figure 155: Bit error rate of Bob in MIMO 4x2 uplink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8) page 103 /

24 Figure 156: Tag detection probability in MIMO 4x2 downlink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8). Figure 157: Tag detection probability in MIMO 4x2 uplink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8) page 104 /

25 Figure 158: in MIMO 4x2 downlink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8). Figure 159: in MIMO 4x2 uplink urban macro-cell environment with 16-QAM modulation and 1/2 coding rate (CQI=8) page 105 /

26 Figure 160: Bit error rate in MIMO 4x2 downlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13). Bit-error-rate Bit-error-rate Figure 161: Bit error rate in MIMO 4x2 uplink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13) page 106 /

27 Figure 162: Tag detection probability in MIMO 4x2 downlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13). Figure 163: Tag detection probability in MIMO 4x2 upnlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13) page 107 /

28 Figure 164: in MIMO 4x2 downlink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13). Figure 165: in MIMO 4x2 uplink urban macro-cell environment with 64-QAM modulation and 3/4 coding rate (CQI=13) page 108 /

29 2.6. Conclusions on tag signals The simulator for testing transmission, reception, and processing of tag signals in LTE has been implemented and extensive simulations have been performed. The transmission, interception, and processing of tag signals was simulated in pre-selected QuaDRiGa scenarios. Namely, we studied indoor office scenario, urban micro-cell scenario, and urban macro-cell scenario. We considered both single- and multiple-antenna scenarios. We have also studied the impact of self-interference caused by tag signals on the detection and decoding of LTE signals with different modulation and coding schemes. Finally, we separately considered downlink and uplink directions. The main observation is that the presence of the tag signal impairs the proper detection of the LTE signal. The tag signal is seen as additional noise in the LTE receiver. However, in some scenarios, the turbo decoder is able to compensate for the effect of the additional noise. Low-order modulation, such as QPSK, is virtually not affected by the presence of additional tag signals. On the contrary, high-order modulations such as 16-QAM and 64-QAM are affected by the presence of additional source of interference. In practice, for 64-QAM modulation, the power of the tag signal needs to be limited to more than 10 db below dominant LTE signal to facilitate reliable decoding of LTE signal. This observation suggest that the power level of tag signal should be adaptively controlled depending on the modulation format. However, advanced and sophisticated decoders are able to cope with additional self-interference caused by tag signals. This fact is reflected in our simulation results where base station decoder performs better than the user equipment decoder. In all simulation cases which we studied, the tag signal power level of 30 db with respect to the LTE signal was too small to guarantee reliable tag detection. Much better results were obtained for tag signals at power levels of 20 db and 10 db with respect to LTE signal. No significant difference in tag signal detection probabilities were observed in different radio scenarios, antenna configurations, and modulation formats. One challenge in the design of tag signals is related to the relatively small number of available Kasami sequences of length chips. Namely, there are only 127 of unique sequences which makes the task of checking all possible sequences relatively easy for Eve, especially in single-antenna channels. In multi-antenna channels, where each transmit antenna can send unique Kasami sequence, the task of the eavesdropper is somewhat more difficult because it needs to identify a tuple of Kasami sequences. More precisely, if Alice uses two transmit antennas, Eve needs to check each of 127*126/2 = possible pairs of Kasami sequences rather than only 127 Kasami sequences. With just 4 transmit antennas, the number of 4-tuples to be checked by Eve exceeds 10 million. In general, the LTE simulation results are similar to WiFi simulation results presented in Deliverable 2.4 and Deliverable 4.1 with respect to the probability of reliable detection of tag signals and tag-signal-based channel estimation. The performance of channel estimation in LTE environment is slightly worse than in the WiFi environment due to longer cyclic prefix and consequently the need to estimate the channel over longer period. Furthermore, in LTE systems, the tag signal needs to be low-pass filtered to meet the strict out-of-band transmission levels. This, in turn, constraints the tuning range of the tag-to-signal-ratio and the relevant design of the lengths and sets of tag signal according to the studies of WP4 task T4.1. More specifically, the practical values of tag-to-signal-ration should be in the range between -20 db and 10 db in order to achieve reasonable channel estimation performance and still meet the out-of-band transmission constraints. In our simulations we have assumed baseline implementation of tag signal processing where tag signal decoder and LTE signal do not exchange any information. We speculate that significant performance improvement, especially in channel estimation and bit error rate performance, is possible when both decoders use self-interference cancellation blocks. More specifically, LTE signal receiver could try to subtract a local replica of the tag signal from the received signal before further processing. Similarly, the tag detection and channel estimation blocks could create a replica of a dominant LTE signal which is later subtracted from the received signal. This should improve the bit error rates of communicating parties and reduce the channel estimation error page 109 /