This paper established a novel multi-input multi-output (MIMO) communication network, in the presence of full-duplex (FD) transmitters and receivers with the assistance of dual-side intelligent omni surface. Compared with the traditional IOS, the dual-side IOS allows signals from both sides to reflect and refract simultaneously, which further exploits the potential of metasurfaces to avoid frequency dependence, and size, weight, and power (SWaP) limitations. By considering both the downlink and uplink transmissions, we aim to maximize the weighted sum rate, subject to the transmit power constraints of the transmitter and the users and the dual-side reflecting and refracting phase shifts constraints. However, the formulated sum rate maximization problem is not convex, hence we exploit the weighted minimum mean square error (WMMSE) approach, and tackle the original problem iteratively by solving two sub-problems. For the beamforming matrices optimizations of the downlink and uplink, we resort to the Lagrangian dual method combined with a bisection search to obtain the results. Furthermore, we resort to the quadratically constrained quadratic programming (QCQP) method to optimize the reflecting and refracting phase shifts of both sides of the IOS. In addition, we introduce the case without a dual-side IOS for comparison. Simulation results validate the efficacy of the proposed algorithm and demonstrate the superiority of the dual-side IOS.

翻译：本文建立了一种新型多输入多输出（MIMO）通信网络，该网络在全双工（FD）收发机场景下，借助双面智能超表面（dual-side IOS）进行辅助。与传统智能超表面（IOS）相比，双面IOS允许来自两侧的信号同时进行反射和折射，从而进一步挖掘超表面的潜力，以规避频率依赖性以及尺寸、重量和功耗（SWaP）限制。通过综合考虑下行链路和上行链路传输，我们旨在最大化加权和速率，同时满足发射机与用户的发射功率约束以及双面反射和折射相位偏移约束。然而，所构建的和速率最大化问题非凸，因此我们采用加权最小均方误差（WMMSE）方法，通过迭代求解两个子问题来处理原问题。针对下行和上行链路的波束赋形矩阵优化，我们利用拉格朗日对偶法结合二分搜索法获得结果。此外，我们采用二次约束二次规划（QCQP）方法优化IOS两侧的反射和折射相位偏移。同时，我们引入了无双面IOS的场景以进行对比。仿真结果验证了所提算法的有效性，并展示了双面IOS的优越性。