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Channel state information (CSI) acquisition is a crucial issue in downlink FDD-based massive multi-input multioutput (MIMO) networks, where the channel reciprocity is not applicable. Thus, users are expected to feedback the bestmatch quantized channels to serving transmitters. Hence, an extensively large size of the feedback overhead is needed, which is linearly scaled at each user with the number...
As recommended by 5th generation Public-Private Partnership (5G-PPP) and Next Generation Mobile Networks (NGMN), one of the most important 5G requirements is to minimize the delay within the network for delay-sensitive services. Main objective of this work is to exploit massive MIMO technology to reduce Hybrid Automatic Repeat Query (HARQ) retransmission delay. Massive MIMO indoor environment is created...
This paper investigates the impact of different sounding bandwidths and center frequencies on power delay profile (PDP) at Terahertz (THz) band based on ray-tracing simulations. The effect of transmit power is also investigated for the center frequencies 300 GHz and 1500 GHz. This approach is based on complex frequency responses extracted for the Transmitter (Tx) and Receiver (Rx) pair, 10 m apart...
Large Scale (Massive) MIMO enhances the advantages of the conventional MIMO in terms of data rate, energy efficiency and reliability. To increase the scalability of conventional massive MIMO, the distributed large scale MIMO is recommended. Synchronization for distributed large scale MIMO is needed due to the lack of common clock source to synchronize the transmitters. Limited Inter-User Connected...
In this paper, the problem of resource allocation in overloaded orthogonal frequency division multiplexing (OFDM) based multiple-input multiple-output (MIMO) cognitive radio (CR) system is considered. The objective is to allocate the different subcarrier and distribute the available user power in order to maximize the CR system throughput. The interference induced to the primary system should not...
Interference alignment (IA) has been proposed to optimally manage the interference aiming at providing the maximum degrees of freedom for multiuser interference channels. Therefore, IA has been used in cognitive radio (CR) systems to perform resource management in order to improve the throughput of the OFDM/FBMC based MIMO CR systems. In this work, a sub-optimal IA based power loading method is proposed...
In this paper, we present a radio resource allocation algorithm based on interference alignment (IA) for orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC) based MIMO cognitive radio (CR) systems. The algorithm provides the opportunity for all secondary users to share the available subcarriers simultaneously using IA technique. Besides, it allocates the power budget...
The main objective of this contribution is to develop a new interference alignment (IA) algorithm, which improves the sum-rate performance of multiuser MIMO communication systems. The recent iterative IA approaches cannot guarantee robust sum-rate performance in different K-user MIMO interference channels, especially at high SNR regime. In our proposed distributed optimization algorithm, each receiver...
The main objective of this contribution is to develop a novel antenna selection algorithm for Interference Alignment (IA) in multi-user communication systems. Successive IA requires high degree of independency among the channels, which could hardly exist in real-world environments. Therefore, the Bit Error Rate (BER) performance of the IA system suffers from a dramatic degradation, especially in indoor...
Main objective of this contribution is to apply Interference Alignment (IA) algorithms in real-world indoor environments for UWB MIMO MB-OFDM communication systems. In indoor environments, the required orthogonality between multiusers channels, which is necessary for proper IA, could be hardly reached. The spatial diversity among the users/nodes is mostly insufficient to obtain a robust performance...
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