## 1 Introduction
Against the deficiencies of the current IA systems above, we propose a novel clustered IA scheme to address the problem of strong interference in the large-scale MIMO networks with limited antennas. The main contributions of this paper may be summarized as follows:  
1. A joint spatial-code clustered (JSCC)-IA scheme is proposed, where the users are partitioned into several small groups. In each cluster, the intra-group interference is eliminated by the traditional spatial IA, and the inter-group interference is suppressed by the orthogonality of the pseudo noise (PN) codes assigned to each group. It is well known that the spread spectrum technique serves as an effective anti-interference approach with the advantage of good confidentiality. Therefore, the proposed IA scheme may serve as a more viable alternative approach to mitigate the interference in large-scale networks. 2. The analytical expressions of the bit error rate (BER) for the proposed JSCC-IA scheme are formulated in the situations of identical and different propagation delays, respectively. To the best of our knowledge, many of the existing literatures investigate the performance of IA from the perspectives of achievable rate or DoFs, while only a few analyze the error performance. As larger sum rate cannot guarantee better QoS, BER is employed as the performance measurement in this paper. 3. Both of the symmetric and asymmetric networks are considered, and a random grouping selection (RGS) algorithm is proposed to search for better grouping combinations for the asymmetric networks. The RGS algorithm can further improve the QoS of the JSCC-IA scheme, and provides a good tradeoff between the computational complexity and performance improvement on BER and received SINR. The rest of the paper is organized as follows: Section 2 presents the related work. Section 3 introduces the system model of a complete binary phase shift keying (BPSK) transmission system in the _K_-user MIMO interference channel. The JSCC-IA scheme is proposed in Section 4, and its mechanism and error performance are analyzed. In Section 5, the random grouping selection algorithm is developed for the asymmetric networks. Simulation results are provided in Section 6, followed by the conclusion and future work in Section 7. Notation: \(\mathbf{A}^{\mathrm{T}}\), \(\mathbf{A}^{\mathrm{H}}\), \(\|\mathbf{A}\|\), real(\(\mathbf{A}\)), sign(\(\mathbf{A}\)) and \(\mathbf{A}(\textit{i,j})\) represent the transpose, the conjugate transpose, the Frobenius norm, the real part, the sign and the element located on the _i_-th row and the _j_-th column of matrix \(\mathbf{A}\), respectively. \(\mathbf{A}_{\textit{i}/}\) denotes the _j_-th column of matrix \(\mathbf{A}_{\textit{k}}\). \(\mathbf{E}[\boldsymbol{\texttt{*}}^{\mathrm{*}}]\), var\([\boldsymbol{\texttt{*}}^{\mathrm{*}}]\) and \([\boldsymbol{\texttt{*}}^{\mathrm{*}}]\) are the expected value, the variance and the ceiling operators, respectively. We employ \(\mathbf{R}\), \(\mathbf{C}\), and \(\mathbf{C}^{\textit{tr}\cdot N}\) to represent the spaces of the real value, the complex value and the \(M\times N\) complex matrices, respectively. The notation rect\({}_{\text{\text{\text{T}}}}(t)\) indicates the unit-amplitude rectangular function with non-zero duration of \(T\). \(\mathbf{CN}(\mu,\ \sigma^{2})\) represents a Gaussian distribution with expected value of \(\mu\) and variance of \(\sigma^{2}\). The notation \((M\times N,\,d)^{K}\) means a \(K\)-user MIMO interference channel where each user transmits \(d\) data streams via \(M\) transmitting antennas and \(N\) receiving antennas.