Earticle

공간다중화 방식을 사용하는 MIMO 시스템은 다이버시티 기법을 사용하는 MIMO 시스템과 비교할 때 높은 전송률을 달성하지만 다이버시티 이득이 낮아 데이터 전송 신뢰도를 높이기 위하여 MIMO 수신단에서 공간정보스트림을 분리해야한다. 본 논문에서는 격자부호에 의한 채널 용량 검출 기법, 사용자 3인인 간섭채널과 선형검출기법인 ZF(Zero Forcing)와 MMSE(Minimum Mean Square Error) 검파 기법을 비교했다. 이때 채널은 Diagonal 채널이 된다. 즉, Diagonal 채널은 로 역행렬이 element-wise inverse로 Jacket 행렬의 성질을 만족함을 확인했다.

Compared to the MIMO system using the spatial multiplexing methods and the MIMO system using the diversity scheme achieved a high rate, but the lower the diversity gain to improve the data transmission reliability should separate the spatial stream at the MIMO receiver. In this paper, we compared Channel capacity detection methods with the Lattice code, the 3-user interference channel and linear channel interference detection methods ZF (Zero Forcing) and MMSE (Minimum Mean Square Error) detection methods. The channel is a Diagonal channel. In other words, Diagonal channel is confirmed by the inverse matrix satisfies the properties of Jacket are element-wise inverse to .

Jacket 행렬[2][8]은 1984년 이문호 교수에 의해 소개되어 신호처리 및 코딩이론에 사용되는† 1 TJ = ⎡⎣Ji−k ⎤⎦ 인 행렬로서, Galois field F 에서 J † 가 J 의 원소별 역행렬일 때†m JJ =mI 의 특성을 갖는 [ ] ik J = j 인 m×m 정방행렬이다. 본 논문에서는 Jacket 행렬에 의해 고유 값으로 분해될 수 있는 정방행렬 2 n A 을 제안하였다. 특히 2 A와 그의 확장인 3 A 행렬을 가지고 쌍곡선과 쌍곡면의 성질을 수정하는데 각각 적용할 수 있음을 보였다. 특히 쌍곡선이 배의 정보량을 갖게 되면 2 A 행렬의 EVD[7]를 이용하여 최종 행렬 2A n 을 쉽게 계산할 수 있다. 또한 여기서제안한 알고리즘을 가지고 컴퓨터 그래픽에서의 응용 프로그램과 수치해석에서도 이용될 수 있음을 보였다.

Jacket matrices[2][8] which are defined to be m ×m matrices † 1 Tik J = ⎡⎣J− ⎤⎦ over a Galois field F with theproperty †m JJ =mI , J † is the transpose matrix of element-wise inverse of J , i.e., † 1 Tik J = ⎡⎣J− ⎤⎦ , were introducedby Lee in 1984 and are used for Digital Signal Processing and Coding theory. This paper presents some squarematrices 2 n A which can be eigenvalue decomposed by Jacket matrices. Specially, 2 A and its extension 3 A canbe used for modifying the properties of hyperbola and hyperboloid, respectively. Specially, when the hyperbola has times transformation, the final matrices 2A n can be easily calculated by employing the EVD[7] of matrices2 A . The ideas that we will develop here have applications in computer graphics and used in many importantnumerical algorithms.

MIMO system performance mainly by correlation of spatial interference presence and space, In order to overcome these effects, it is necessary by pre-coding technology to achieve, Here we mainly use block diagonalization algorithm both linear pre-coding for analysis and further integration of the average power allocation algorithm and power allocation algorithm MATLAB simulation. Through research and data analysis, system performance block diagonalization algorithm is better than under zero forcing algorithms. When the same number of transmit antennas, the number of receive antennas are the same, the average power and power calculation algorithm with SNR increases, can effectively channel capacity to achieve the desired optimum value.

In this paper, we present the block diagonalization (BD) algorithm for the multiple-user multiple input multiple output (MU-MIMO) $4{\times}4$ system using specific purpose processor (SPP) hardware. Our objective is to improve the single-user MIMO (SU-MIMO) system using the MU-MIMO technology, which is remarkably fast and allows more users to connect simultaneously. To that end, our MU-MIMO precoder uses the BD algorithm to ensure signal integrity when connecting multiple users; but remains accurate and stable. However, a precoder that uses the BD algorithm is computationally complex; therefore, we use an SPP with special functions designed to compute the BD algorithm. The implementation test results show that our SPP computes the BD algorithm faster than the software solution.

Block diagonalization (BD) has been proposed as a simple and effective technique in multiuser multiple-input multiple-output (MU-MIMO) broadcast channels. However, when channel state information (CSI) knowledge is limited at the transmitter, the performance of the BD may be degraded because inter-user interference cannot be completely eliminated. In this paper, we propose an efficient CSI quantization technique for BD precoded systems with limited feedback where users supported by a base station are selected by dynamic scheduling. First, we express the received signal-to-interference-plus-noise ratio (SINR) when multiple data streams are transmitted to the user, and derive a lower bound expression of the expected received SINR at each user. Then, based on this measure, each user determines its quantized CSI feedback information which maximizes the derived expected SINR, which comprises both the channel direction and the amplitude information. From simulations, we confirm that the proposed SINR-based channel quantization scheme achieves a significant sum rate gain over the conventional method in practical MU-MIMO systems.

A femtocell is a small cellular base station, typically designed for use in a home or small business. The random deployment of a femtocell has a critical effect on the performance of a macrocell network due to co-channel interference. Utilizing the advantage of a multiple-input multiple-output system, each femto base station (FBS) is able to form a cluster and generates a precoding matrix, which is a modified version of conventional single-cell block diagonalization, in a cooperative manner. Since interference from clustered-FBSs located at the nearby macro user equipment (MUE) is the dominant interference contributor to the coexisting networks, each cluster generates a precoding matrix considering the effects of interference on nearby MUEs. Through simulation, we verify that the proposed algorithm shows better performance respective to both MUE and femto user equipment, in terms of capacity.

Nonconvex Quadratic Optimization Problems (QOP) are solved approximately by SDP (semidefinite programming) relaxation and SOCP (second order cone programmming) relaxation. Nonconvex QOPs with special structures can be solved exactly by SDP and SOCP. We propose a method to formulate general nonconvex QOPs into the special form of the QOP, which can provide a way to find more accurate solutions. Numerical results are shown to illustrate advantages of the proposed method.

본 논문에서는 다중 사용자 증폭재전송(amplify-and-forward) MIMO 중계 시스템을 위한 프리코딩(precoding) 기법을 제안한다. 제안된 프리코딩 기법에서는 블록 대각화(block diagonalization)를 통해 사용자간 간섭(inter-user interfereence)을 제거하고 기지국과 중계기에서의 전송전력(transmit power) 제한을 만족하면서 사용자 데이터 전송률(data rate)의 합이 최대화 되도록 한다. 모의실험결과를 통해 제안된 기법이 기존 기법에 비해 더 높은 사용자 데이터 전송률의 합을 달성함을 보이고 있다.