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Effect of bit resolution value (B bits) on the total sum rate loss with different SNR values (SNR = 35, 25, 15 dB.)
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Interference Alignment (IA) emerged on the communication scene as a solution to the interference problem in all interference-limited networks, including heterogeneous cellular systems. However, the performance of IA is greatly related to the accuracy of the channel state information at transmitters
(CSIT)(namely the number of feedback bits). Thus,...
Citations
... 据此分析, w sn 和 τ s0 可通过求解方程 (22) 得到: ...
... With the development of the fifth-generation (5G) mobile communications, the number of connected devices has dramatically increased, which exploits the need for extra frequency spectrum resources [1]- [3]. Dual-function radar and communication (DRC) system, that integrates both radar and communication systems together within one system, has been proposed as a promising approach to improve spectrum utilization [4]- [7]. ...
Dual-function radar and communication (DRC) system has been recently recognized as a promising approach to solve the spectrum scarcity problem. However, when the target exists within a crowded area where pathloss dominating, the performance of radar may be severely degraded. To tackle this issue, this paper proposes for the first time the deployment of an intelligent reflecting surface (IRS) to help the DRC system to enhance radar detection performance. The IRS can configure the environment around the radar by adaptively adjusting the phases of its reflecting units to strengthen the signal quality towards specific directions, mostly the target direction, and completely null-out transmissions in other directions, mostly the directions towards communication system. Specifically, we investigate the joint optimization of the IRS passive phase-shift matrix (PSM) and precoding matrix of the radar-aided-basestation (radar-BS) for DRC system. The optimization is carried-out through maximizing the signal-to-noise ratio (SNR) at the radar receiver under both sensing and communication constraints, which turns out to be a non-convex problem. In order to circumvent this challenging problem, an alternation optimization approach is employed to decouple the optimization variables and split this
intractable problem into two sub-problems. However, it is still challenging to obtain the optimal PSM due to the high power of the objective function and the unit-modulus constraints. To solve this problem, a majorization-minimization algorithm is conceived to transform the non-convex problem to an easy to solve quadratic constraint quadratic programming problem. Simulation results demonstrate that the IRS can help improving
the performance of DRC system in terms of the received SNR, and the proposed algorithm shows fast convergence.
... Accordingly, maximum degrees of freedom (DoFs) for the whole network can be achieved. The IA scheme is studied for many different networks, e.g., X channel [10], K-user interference channel (IC) [8,11], heterogeneous networks [12][13][14], and cognitive radio networks [15,16]. Moreover, the importance of the channel state information (CSI) for successful IA implementation is addressed in many works [17,18]. ...
... Accordingly, designing IA precoding and decoding filters with no CSI available at the BS, however, it is non-optimal, but it is considered in many practical scenarios because of its reduced system overhead of acquiring the global CSI from all the system nodes. One of the possible solutions to (12) is to choose V, as V = I M . Choosing V in this form means that the BS broadcasts user messages without any processing, which reduces the overhead due to handshaking messages in acquiring and forwarding CSI in the network. ...
... Additionally, we assume that perfect full global CSI is available at the BS. In other words, the BS owns a copy of the channel between it and each user in the system [8][9][10][11][12][13][14][15][16][17][18]32]. The simulation results are obtained through averaging the measurements over 5000 channel realizations. ...
Interference alignment (IA) and non-orthogonal multiple access (NOMA) are key technologies for achieving the capacity scaling required by next generation networks to overcome the unprecedented growth of data network traffic. Each of these technologies was proved to present excellent performance for MIMO systems. In this article, we propose a joint IA and power allocation (PA) framework for NOMA-based multiuser MIMO (MU-MIMO) systems. Different approaches for applying IA in downlink NOMA-based MU-MIMO systems will be addressed while implementing a PA technique that fully exploits the characteristics of NOMA-based systems. The proposed framework aims to maximize the sum-rate of the NOMA-based MU-MIMO system through combining IA with PA. The process begins by initially grouping the system users into clusters for optimum implementation of NOMA. The sum-rate maximization is carried out under cluster power budget, user quality-of-service (QoS), and robust successive interference cancellation (SIC) constraints. Meanwhile, it uses the power domain multiplexing strategy to allow the users within each cluster to share the data streams without exerting interference to one another. Three iterative joint IA and PA algorithms are proposed for NOMA-based MU-MIMO systems. Moreover, these algorithms are compared with orthogonal multiple access (OMA)-based MU-MIMO counterpart as well as the state-of-the-art techniques presented for NOMA-based MU-MIMO systems. Numerical simulations verify that the proposed framework can greatly improve the performance of NOMA-based MU-MIMO systems in terms of the achievable sum-rate when compared with OMA-based MU-MIMO and the state-of-the-art NOMA-based MU-MIMO systems.
... Accordingly, maximum degrees-of-freedom (DoFs) for the whole network can be achieved. The IA scheme is studied for many different networks, e.g., X channel [10], K−user interference channel (IC) [8,11], heterogeneous networks [12]- [14], and cognitive radio networks [15,16]. Moreover, the importance of the channel state information (CSI) for successful IA implementation is addressed in many works [17,18]. ...
... Accordingly, designing IA precoding and decoding filters with no CSI available at the BS, however it is non-optimal, but it is considered in many practical scenarios because of its reduced system overhead of acquiring the global CSI from all the system nodes. One of the possible solutions to (12) is to choose V, as V = IM . Choosing V in this form means that the BS broadcasts user messages without any processing, which reduces the overhead due to handshaking messages in acquiring and forwarding CSI in the network. ...
... Choosing V in this form means that the BS broadcasts user messages without any processing, which reduces the overhead due to handshaking messages in acquiring and forwarding CSI in the network. Accordingly, the decoding filters can be evaluated by substituting in (12) as ...
Interference alignment (IA) and non-orthogonal multiple access (NOMA) are key technologies for achieving the capacity scaling required by next generation networks to overcome the unprecedented growth of data network traffic. Each of these technologies was proved to present excellent performance for MIMO systems. In this article, we propose a joint IA and power allocation (PA) framework for NOMA-based multiuser MIMO (MU-MIMO) systems. Different approaches for applying IA in downlink NOMA-based MU-MIMO systems will be addressed while implementing a PA technique that fully exploits the characteristics of NOMA-based systems. The proposed framework aims to maximize the sum-rate of the NOMA-based MU-MIMO system through combining IA with PA. The process begins by initially grouping the system users into clusters for optimum implementation of NOMA. The sum-rate maximization is carried out under cluster power budget, user quality-of-service (QoS), and robust successive interference cancellation (SIC) constraints. Meanwhile, it uses the power domain multiplexing strategy to allow the users within each cluster to share the data streams without exerting interference to one-another. Three iterative joint IA and PA algorithms are proposed for NOMA-based MU-MIMO systems. Moreover, these algorithms are compared with orthogonal multiple access (OMA)-based MU-MIMO counterpart as well as the state-of-the-art techniques presented for NOMA-based MU-MIMO systems. Numerical simulations verify that the proposed framework can greatly improve the performance of NOMA-based MU-MIMO systems in terms of the achievable sum-rate when compared with OMA-based MU-MIMO and the state-of-the-art NOMA-based MU-MIMO systems.
In this paper, we propose a limited feedback-based interference alignment (IA) scheme suitable for two tier
macrocell-femtocell heterogeneous networks. Firstly, an analytical expression for the total system sum rate loss due to the employment of limited feedback channel versions is derived. Then, a comparative simulation study is done between two IA schemes that are employed in our proposed limited feedback system, namely, Hierarchical IA (HIA) scheme and Iterative Reweighted Least Squares (IRLS) IA scheme. Simulation results
confirmed the severe effect of quantization of CSI on the IA performance. Additionally, the obtained results show that the IRLS based IA scheme is more robust to quantization errors that the HIA scheme.
