In this paper, we study the sum degrees of freedom (DoF) of an uplink two-cell multiuser MIMO interference network with asymmetric number of users in the cells. The achievable DoF is devised based on a two-dimensional space-time spreading code framework with linear precoding/decoding design and finite channel extension. The derivation of the achievable DoF is shown related to a rank minimization problem, which corresponds to the minimization of the dimension of the interference subspace. The problem is solved by the proposed grouping algorithm (GA) based on aligning interfering signals into a low-dimensional subspace as a group and attaining the minimum number of groups. The achievable sum DoF derived based on the proposed GA is shown to be greater than prior arts and achieves the theoretic upper bound in several cases. We also give a closed-form expression of the maximum achievable sum DoF when there is the maximum number of admissible users in the considered finite diversity environment.

This study explores the convergence property of the interference leakage minimization algorithm (ILMA) proposed by S. W. Peters and R. W. Heath Jr. Among the cooperative interference alignment (IA) schemes, we specialize the ILMA for its better feasibility in real applications than others. However, ILMA cannot converge to a global minimum because of its unitary constraints. As an iterative algorithm, no guarantee of global convergence means the tendency towards system instability. Up to the present, most efforts on cooperative IA are focused on pursuing high sum rate or low error floor. None of existing works looks into the convergence issue. In this paper, we explore the global convergence of ILMA. Specifically, we devise a constraint relaxation version of ILMA, named rILMA. Except that rILMA inherits the benefits from ILMA, mathematical proofs given in this paper also show that rILMA converges to a global minimum. This exploration directly results in the improvement of performance. Numerical simulations show that rILMA outperforms Max-SINR and ILMA in sum rate performance.

In this paper, we study in depth the alternating minimization algorithm (AMA) proposed by S. W. Peters and R. W. Heath Jr. Among iterative interference alignment (IIA) schemes, AMA is considered more feasible for real applications than other IIA schemes. However, no research has investigated how AMA is affected by some real system characteristics. Motivated by this, we look into the effects when taking into account the initial conditions and the final conditions of AMA in the MIMO interference networks. We found the sum rate performance of AMA can be improved by well exploiting its initial conditions. On the other hand, by considering the interference leakage residue (ILR) which is the final condition of AMA, we derived a more realistic formulation which fast calculates the smallest possible codebook for precoders.

To exploit the multiplexing and the diversity gains in a multiple-input multiple-output (MIMO) wireless system, the singular value decomposition (SVD) is typically used to decompose the MIMO channel into parallel eigen-modes. However, the dynamics of the frequency selective channel fading variations often make the decomposed subchannels ill-conditioned and lead to unsatisfied performance in terms of bit error rate (BER). Recently, two improved SVD-based decomposition schemes, i.e. the geometric mean decomposition (GMD) and the uniform channel decomposition (UCD), are developed which try to achieve uniform subchannel gains. Unfortunately, both of them require even higher complexity than that of SVD with power allocation. In this paper, we propose a decomposition method, called LDLHwhich has moderate subchannel gains uniformity while exhibits the lowest complexity compared with that of aforementioned SVD-based schemes. Simulations are also given to show that the proposed LDLHscheme is more robust than all other schemes at higher SNR as channel estimation errors become severe.

In classical MIMO systems, singular value decomposition (SVD) is adopted as a common way to decompose the MIMO channel into parallel subchannels. However, in addition to its high complexity, it is also sensitive to the ill-conditioning of the channel matrix. In this paper, we propose a channel decomposition strategy called LDLHdecomposition for low complexity MIMO precoder design. We show that the computation complexity of LDLHachieves one degree of magnitude lower than that of SVD, as well as some SVD-based decomposition scheme such as geometric mean decomposition (GMD) and uniform channel decomposition (UCD). In addition, we also show that LDLH-based precoder requires less quantization effort and feedback bandwidth.