# A Pilot-Pattern Based Algorithm for MIMO-OFDM Channel Estimation
## 1 Introduction  
Channel estimation is a critical prerequisite for the MIMO-OFDM system, which is a core technology for the 4 th generation mobile communication system (4G) and beyond 4G [1; 2]. It acquires the required channel information in advance with the aim of making the piloide code of the transmitter more efficient so as to make the receiver detect signals more effectively [3; 4]. Therefore, the accuracy of channel estimation is the most critical in the concerns that determine the overall performance of a MIMO-OFDM system [5; 6]. The general procedure of channel estimation, as shown in Figure 1, is to figure out how an input signal can be quantitatively influenced by a physical channel, which is instinctively a trial to establish a mathematical expression involving all of the influences existing in the physical channel [7]. For the simplest case, i.e., where a channel is a linear one, the estimation implements issues focusing on the system response to impulse excitation [8]. However, the procedure of channel estimation, in practice, is complicated and comprehensive, in which several factors must be considered. The first factor is the accuracy which reflects algorithm's capability to minimize the estimated error that is based on the mean square. Additionally, the complexity and overhead, which influence the real-time performance, is somewhat determined by some necessary devices [9]. As these two factors are contradictory, i.e., the more accurate the estimation, the more complicated the corresponding algorithm, channel estimation is turned into a process of exploring appropriate algorithms to reach the most optimized balance point between them. The algorithms for channel estimation are categorized as three types in concept: non-blindness algorithm, blindness algorithm, and half-blindness algorithm [10]. The non-blindness algorithm is suited for the pilot signal or on a training sequence that is dependent on the pilot signal sent by the transmitter and known by the receiver to focus on the channel status information. Although theproblem exists in which the efficiency of a channel accommodation that is counted in bytes will be reduced somewhat, the non-blindness algorithm is broadly implemented, in practice. In contrast to the non-blindness algorithm, the blindness algorithm is based on the characteristics of a channel's statistics that are dependent on the intrinsic math information carried by the transmitted data, but not on any pilot signal. The half-blindness algorithm, it inherits the pros and cons of both the blindness algorithm and the non-blindness algorithm [11; 12]. It is noted that the non-blindness algorithm is the simplest and most mature among the aforementioned three algorithms when used for MIMO-OFDM systems. It usually adopts the least square (LS), the minimum mean square error (MMSE), and the maximum likelihood (ML) as the criteria to describe the instant status of the channel. ML can be verified by the optimal pilot sequence of MIMO-OFDM on the condition that the channels with segmental fading shall be orthogonal to each other. MMSE is validated by the design method for the channel with frequency-selected fading that can make the power of transmitter reach the lower bound of the average level. For the cases that apply the optimal pilot sequence to the channel with segmented fading or apply ML to the channel with continuous fading, it is impossible to just use quite a few pilot signals to give an accurate channel estimation, which shows that the accuracy of channel estimation is contradictory to the spectrum efficiency. On the other hand, the experimental implementation of channel estimation on the MIMO-OFDM, which is based on V-blast with a known bit error rate, can deliver the optimal pilot interval and pilot length to guarantee the best efficiency of communication. The study on the LS method for non-blindness channel estimation in MIMO-OFDM provided several effective methods to implement estimation for the channel with frequency-selective fading.