Due to the high-power consumption and complexity of fully digital baseband Precoding, its implementation in massive millimeter-wave multiple-input multiple-output (MIMO) systems is not cost-efficient and practical; for this reason, hybrid Precoding has attracted a lot of attention in recent years.  Most hybrid Precoding techniques concentrate on the fully-connected structure, although they require lots of phase shifters, which is high energy-consuming. On the contrary, the partially-connected structure has low power consumption, nevertheless, suffers from a severe decrease in spectral efficiency (SE). To enhance SE, this paper proposed a dynamic hybrid Precoding structure where a switch network is able to provide dynamic connections from phase shifters to radio frequency (RF) chains. To determine the digital precoder and the states of switch, a novel alternating minimization algorithm is proposed, which leverages closed-form solutions at each iteration to efficiently converge to an optimal solution. Furthermore, the phase shifter matrix is optimized through an iterative solution. The simulation results show that in terms of SE, the proposed algorithm with a dynamic structure achieves higher performance than the partial structure. Also, since the proposed structure reduces the number of phase shifters, it can guarantee better energy efficiency (EE) than the fully connected structure.

Orthogonal frequency division multiplexing (OFDM) is an attractive approach for multi-channel transmission. Due to some advantages such as high spectral efficiency, simplicity in channel equalization, and robustness to the fading channel, the OFDM technique has been generally used in many wireless communication devices. Besides these advantages, the OFDM systems usually experience a high peak-to-average power ratio (PAPR). As a result of the high PAPR, the OFDM signal is clipped during the passing via a non-linear power amplifier. The Precoding techniques decrease the autocorrelation of the input data symbols to mitigate the high PAPR. In this paper, we compare WHMT, VLMT, DCMT, DHMT, DFMT, ZCMT, and CVMT Precoding approaches in terms of autocorrelation and PAPR reduction performance. We demonstrate that the serial combinations of both ZCMT and CVMT Precoding matrices with the IFFT matrix cancel the impact of each other when their dimensions are the same. In this case, a sparse matrix is produced and hence the PAPR of the OFDM signal is remarkably reduced. This issue is proved by the mathematical calculations and verified by the simulation results. It is also presented that DFMT, ZCMT, and CVMT Precoding schemes have almost the same PAPR reduction performance and they are the best among their counterparts.

In this paper, the performance of a system in terms of the energy efficiency (EE) is studied. To check the EE performance, an appropriate power is allocated to each user. The system in question in this paper is a multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (NOMA) method. Precoding in this system is considered to be the zero forcing (ZF). It is also assumed that the channel state information (CSI) mode is perfect. First, all the parameters that affect the channel, such as path loss and beam forming are investigated, and then the channel matrix is obtained. To improve system performance, better conditions are provided for users with poor channel conditions. These conditions are created by allocating more appropriate power to these users, or in other words, the total transmission power is divided according to the distance of users from the base station (BS) and the channel conditions of each user. The problem of maximizing the EE is formulated with two constraints of the minimum user rate and the maximum transmission power. This is a non-convex problem that becomes a convex problem using optimization properties, and because the problem is constrained it becomes an unconstrained problem using the Lagrange dual function. Numerical and simulation results are presented to prove the mathematical relationships which show that the performance of the proposed scheme is improved compared to the existing methods. The simulation results are related to two different algorithms with a same objective function. Furthermore, to comparison with performance of other methods, output of these two algorithms are also compared with each other.

Presently, due to emergence of new generation of wireless telecommunication networks, some appropriate capacity and coverage have been provided for end-users by new hybrid terrestrial-satellite networks, consisting of two or more satellites in different orbits and terrestrial equipment. Today, due to the lack of spectral resources, a method, such as cognitive radio is used to allow for coexistence of spectrum between different nodes. Therefore, in this paper, spectral coexistence method between two satellites was applied over a common region based on cognition link to manage energy efficiency. Also, for mitigating interferences between satellites in downlink channel, the Stackelberg game was exploited. According to simulation results, the proposed algorithm for a primary satellite system with a main node had more energy efficiency compared to the other algorithms, such as sequential convex approximation (SCA)-based Precoding, multi-beam interference mitigation (MBIM), and zero-forcing (ZF)-based Precoding.

This paper studies interference alignment scheme and minimum mean square error (MMSE) improvement in Internet of Things (IoT)-oriented cognitive systems, where IoT devices share the licensed spectrum by cognitive radio in spectrum underlay. Target to manage the intertier interference caused by cognitive spectrum sharing as well as ensure an MMSE at receivers, the interference alignment algorithms is proposed. In particular, we focus on the problem of designing the optimal linear pre-coding to minimize the total mean square error while satisfying transmit power constraints. Firstly, we introduce a system model of the downlink IoT-oriented cognitive multi-input multi-output (MIMO) system. Secondly, we propose an interference nulling based cognitive interference alignment scheme, and then, the pre-coding and post-coding matrix designs for the primary transceivers to minimum mean square error (MSE), as well as to eliminate the cochannel interference to the primary receivers. We also apply these interference alignment scheme and matrix designs for the secondary links. Finally, the numerical results are used to evaluate performance of the proposed algorithm.

Millimeter wave communication (mmWC) is a promising volunteer for 5G communication systems with high data rates. To subdue the channel propagation characteristics in this frequency band, high dimensional antenna arrays need to be deployed at transceiver. Employing such a deployment, prevents to use of ADC or RF chain in each branch of MIMO system because of power constraints. Thus, Such systems impose to have a hybrid analog/digital Precoding/combining architecture. Hence, channel estimation revision seems to be essential. This paper propose new algorithms to estimate the mmW channel by exploiting the sparse nature of the channel and finding the subspace of received signal vectors based on MUSIC. By combining the multiple measurement vector (MMV) concept, MISIC, subspace augmentation (SA) and two-stage orthogonal subspace matching pursuit (TOSMP) approaches, we try to recover the indices of non-zero elements of an unknown channel matrix accurately even under the defective-rank condition. These indices are called support in the context. Simulation results indicate MUSIC-based approaches offer lower estimation error and higher sum rates compared with conventional MMV solutions.