MIMO (Multiple Input Multiple Output) is expected to become popular as a solution to enhance data throughput and quality of communication in complex propagation environments.
The technology has attracted a great deal of attention recently because of the critical role it plays in the IEEE 802.11n standard that is nearing completion with "pre-n" system-level products already on the market. But there are several different interpretations and implementations considered to be MIMO technology and MIMO will be adopted in many of the wireless and mobile communication systems in the future.
This article discusses the IEEE 802.11n system currently being proposed and explores the expected design and measurement challenges for a specific type of MIMO system implementation.
The IEEE 802.11n system promises to have enhanced data rates, better spectral efficiency, better quality and more robust system when compared to the existing IEEE 802.11a/b/g system. Several ideas have been considered to support these requirements.
Data rates have been enhanced by increasing the number of sub-carriers from the 54 sub-carriers used in 802.11a/g systems to 114 sub-carriers. However, this requires slightly over twice the current occupied spectrum and does not contribute to the robustness or spectrum efficiency.
Therefore, it was decided to adopt the MIMO technology in IEEE 802.11n standards. MIMO is a family of techniques for multi antenna wireless transmission and reception that increases the achievable data throughput within the same occupied bandwidth, increases quality of communication, and allows dramatically increased spectral efficiency.
While offering substantial benefits to system performance, it also increases the challenges in design and system evaluation and validation. New measurements need to be considered for testing MIMO systems
Design challenges of MIMO systems
There are many was to accomplish MIMO processing, including MIMO Multiplexing, MIMO Diversity and others. IEEE 802.11n will adopt Spatial Division Multiplexing (SDM) which is a form of MIMO Multiplexing.
The diversity gain and throughput are improved through the use of multiple antennas and specialized coding schemes. In principal, increasing the number of antenna branches enables data throughput to increase geometrically with the increasing number of antenna pairs. IEEE 802.11n will support up to 4 transmitting antennas.
While there are multiple MIMO configurations, this article will examine the use of a 2X2 MIMO system (2 transmitting antennas and 2 receiving antennas).
Figure 1 diagrams the sequential multiplexing of packet data as it is routed from baseband to the multiple antenna for the 2X2 MIMO system. Different data packets are transmitted (Tx1 and Tx2) on each antenna, and signals are combined in free space environment.
Spatial diversity and the multipath propagation are important elements of the MIMO implementations, and the important challenges in the design of the system. At the receiver end of the system, a combination of the multiple transmission paths are received at each of the receive antenna (Rx1 and Rx2).
Click here for Figure 1
Figure 1:Example of a typical 2x2 MIMO system.
The channel characteristics between the antenna branches will be different. The physical separation distance (spatial diversity) and the spatial fading correlation coefficient between antenna branches has influence on the data throughput.
Designs will be required to take special care of each antenna position to get minimum spatial fading correlation. In the receiver (Rx1 and Rx2), the Tx1 and Tx2 signals must be split from each of the received signals so the transmitted data can be restored to the original Tx1 and Tx2 signals. The probability of increasing the quality of transmission improves by increasing the number of receiving antennas.
In order to improve the efficiency of transmission for a MIMO system, a key performance parameter will be how accurately the receiver can split the transmitted signals by using the different propagation of characteristics of each channel.
The designer must not only consider the characteristics of transmitter and receiver; but it also necessary to design systems architecture tolerant of the channel propagation characteristics. In a typical environment (office or home), the channel propagation characteristics will vary dramatically from moment to moment as 'mobile interferers' such as people move about the room.
In consideration of such a dynamically changing propagation environment, calculation of the channel propagation characteristics of every packet is required to optimizing transmission. Therefore, advanced algorithms for optimization must be accurate, adaptive, and computationally fast.