Analyses of System Performance in Multi-cell Environment

In order to reduce interferences, directional antenna can be used [15]. When S (a small integer) directional antennas are used at a base station to illuminate S different sectors in a cell, the interference can be reduced by a factor of S. This reduction can be defined as sectored antenna gain G . Another approach for interference reduction is the relatively low speech _s

activity factor, which is about 40% during two-way conversations [21]. This can be defined as voice activity gain G_v.

By Gaussian approximation, the interference signal can be modeled as Gaussian noise;

hence the discussion of the system performance in multi-cell environment can be simplified.

It should be noticed that in our MIMO systems, the interference signal power received at each receive antenna is proportional to the number of transmit antenna M. For example, in our simulations M =4 , a -5 dB SIR is equivalent to a -11dB by Gaussian simulation results of the SIR distributions in paragraph 4.2, we can obtain the simulated distribution of user’s average error probability at two tiers’ multi-cell environment (ignoring the effect of noise) in Figure 5.12. We can see that MIMO MC-CDMA systems outperform MIMO-OFDM systems in both downlink and uplink channels.

Figure 5.12: Simulated distribution of user’s average error probability at two tiers’ multi-cell environment

Chapter 6

Conclusions

High data rate and spectral efficiency is a major demand for the future generation of wireless communication systems. Many attractive candidates of transmission schemes are based on OFDM. In this paper, we conduct a comparison of the MIMO-OFDM and MIMO MC-CDMA systems. For MIMO-OFDM system, we adopt the V-BLAST detection and convolutional coding technique; for MIMO MC-CDMA system, we use an iterative multi-layered detection method combined with a MPIC technique to suppress the interference induced by multi-layered transmission and multipath environment. We observe that MIMO MC-CDMA significantly surpasses MIMO-OFDM at low SNR region. At high SNR region, MIMO-OFDM slightly outperforms MIMO MC-CDMA. In Chapter 4, a multi-cell environment is introduced hence we can extend our investigations to more realistic scenario, i.e., cellular structures. Simulations show a good match by replacing the interfering signals with the Gaussian approximation. From computer simulations of the SIR distributions we can obtain the user’s average error probability in a multi-cell environment for both MIMO-OFDM

and MIMO MC-CDMA systems. Simulation results show that MIMO MC-CDMA has a better performance in a multi-cell environment.

APPENDIX A

The Modified MMSE V-BLAST Equalizer

Our goal is to design the equalization matrix G to minimize the mean square error, namely

{

}

E x Gz− where x is the transmitted signal vector and z is the received signal vector after matching in frequency domain as described in subsection 3.1. Each component of x has zero mean and unit variance, i.e., ^E

{ }

and variance σ , i.e., _n² E

{ }

( )

{

}

E x Gz z− = (A-1)

Expand the equation, the first term becomes:

{ } { } { }

The second term becomes:

{ } { ( )( ) }

(

)

= +

G H HH H H H H H (A-4)

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