, (3.10)
where γ is vector [r1, r2, ...rN] of the per-tone SINR values, ref f is the effective SINR mapping, whileβ is a parameter dependent on the adopted MCS.
When doing system level simulations, we no longer worry the perfect channel con-dition at the link level. It is only necessary to know AWGN performance of different modulation and coding rates and their corresponding factorsβ. For our simulations, a set ofβ calibrated for link-level interface in the IEEE 802.16e (WiMAX) is adopted and listed in Table 3.2 [25]. Therefore, interface from link level to system level simulation will be greatly simplified. Once the SINR at a given location is calculated as described above, spectral efficiency (SE) can be obtained for a dedicated MCS and throughput is directly related to the corresponding spectral efficiency (SE). Then the system performance can be measured by using the EESM method.
3.3 Problem Formulation
In future cellular networks, base stations will be equipped with multi-antenna and adopt MIMO techniques to provide higher data throughput and link reliability for better ser-vices or new applications. By using spatial multiplexing (SM), base stations can offer huge throughput for mobile station at neighboring area. The total transmit power of the base station is split uniformly across transmit antennas, and it leads to lower SINR. Since SM needs high SINR for reliable detection, the throughput of mobile stations around cell edge decreases seriously, it is even worse than using spatial diversity (SD). Hence, the cell coverage of the serving base station is reduced. To cover the service area, the telecommu-nications operators need to set up more base stations, this causes higher equipment cost and increases the handover frequency of mobile station due to smaller cell size. In reality,
Table 3.2: Reference EESMβ Values for ITU Pedestrian B Channel Code Rate Spectrum Minimum EESM Modulation (Repetition: Efficiency SINR factor
default=1) σ (bit/s/Hz) β
QPSK 1/2(4) 0.25 -2.5 dB 2.18
QPSK 1/2(2) 0.5 0.5 dB 2.28
QPSK 1/2 1 3.5 dB 2.46
QPSK 3/4 1.5 6.5 dB 2.56
16-QAM 1/2 2 9 dB 7.45
16-QAM 3/4 3 12.5 dB 8.93
64-QAM 1/2 3 14.5 dB 11.31
64-QAM 2/3 4 16.5 dB 13.8
64-QAM 3/4 4.5 18.5 dB 14.71
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spatial correlation can occur at a base station or mobile stations due to insufficient separa-tion and scattering environment, and the correlasepara-tion causes diversity loss and performance degradation. For SM especially, the increased throughput depends upon the fact that the channels from a transmitter to a receiver follow independent paths. Increasing the linear dependence of the input streams response make it more difficult to separate streams and decode signals. The impacts of spatial correlation in SM MIMO systems is more dramatic than that in SD MIMO systems.
In this thesis, we want to get the multiplexing gain of SM and improve the per-formance around the cell edge. Due to the limitation of transmit power for avoiding in-terference and system constrains, increasing transmit power or transmit antennas is not a feasible way to overcome this problem. Therefore, we purpose to apply transmit diversity at the base stations sides for increasing SINR of mobile station at the cell area. With MDHO, when the mobile station moves around the cell edge, the adjacent base stations in diversity set are synchronized to transmit the same signals at the same time over the same frequency to mobile station, seen as a DSFN. SFBC and CDD techniques are adopted for transmit macro-diversity at the base station sides. Each base station performs SM and the impacts of spatial correlation are considered. The performances of the transmit macro-diversity combining with SM are investigated and discussed with simulation results.
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CHAPTER 4
Space-Frequency Block Code Macro-Diversity Combining for MIMO OFDM Cellular Mobile
Networks
Spatial multiplexing (SM) can be applied as a promising and powerful method to dramati-cally increase the system capacity. In a rich scattering environment, the independent spatial channels can be exploited to transmit multiple signals at the same time and frequency re-sulting in higher spectral efficiency. Due to path loss and spatial correlation, the SINR of mobile station degrades when it moves away from the serving base station, and thus decreasing throughput seriously. Spatial Diversity (SD) methods can be used to improve signal quality, which is more robust than SM for the spatially correlated channel. Alamouti transmission structure [16] can be applied in the space-time and space-frequency domain in OFDM systems. By using STBC and SFBC in OFDM cellular networks, the SINR can be increased at the receiver and cell coverage is extended. In contrast to this, using SM in the OFDM cellular networks reduces the cell coverage and needs high receive SINR for reliable detection. Thus it is required to combine the SM and SD MIMO techniques in the cellular network. Both spatial diversity and spatial multiplexing gains can be achieved at farther locations from the serving base station. Because the encoding of SFBC is done across the sub-carriers inside the OFDM symbol and STBC applies encoding across the OFDM symbol, there is an inherent processing delay unavoidable in STBC system. Delay
spreads in frequency selective fading channels destroy the orthogonality of the receive sig-nals [31]. Thus in OFDM systems the sub-carrier spacing is usually small and symbol time is long. Thus it is more reasonable to use SFBC instead of STBC in the OFDM cellular networks with moving mobiles. In this chapter, we introduce the space-frequency block code macro-diversity combining with spatial multiplexing scheme.
4.1 SFBC Macro-Diversity Scheme
In this section, we introduce the space-frequency block code macro-diversity combining to improve the throughput of the mobile station around cell edge with the cooperation of the adjacent suitable base stations. When the mobile station moves from the serving base station (BS 0) to the target base station (BS 1) as depicted in Fig. 3.1. When reaching the cell boundary, the mobile station receives the signals from two groups of antennas located at adjacent base stations by MDHO. The adjacent base stations transmit the same data encoded by SFBC at the same frequency as depicted in Fig. 4.1. The receiver of the mobile station can performs diversity combining to get the diversity gain. Hence we can increase the SINR of the mobile station around cell edge to provide higher throughput. At the receiver, each antenna receives the MIMO OFDM signals. The guard interval is removed and fast Fourier transform on each receive antenna is performed to get the output signals of OFDM demodulation.