Spectral And Energy Efficiency Analysis For Beamforming Optimization Objectives For Wireless Massive MIMO Systems

Recently, massive multiple input multiple output (MIMO) is considered as a promising technology to significantly improve the spectral and energy efficiency of fifth generation (5G) networks. Massive MIMO increases the energy efficiency (EE) by three orders of magnitude as compared to single antenna systems. In this paper, we review and analyze energy efficient resource allocation techniques for massive MIMO system. To this end, the energy efficiency of massive massive MIMO with linear processing is formulated. Analytical formulation is provided for the spectral efficiency (SE) and total system power consumption model. The tradeoff on the spectral and energy efficiency is analyzed. The impacts of propagation and system parameters on the SE-EE tradeoff are investigated. Then, large scale fading based analytical approximation for the energy efficiency is formulated. Finally, numerical simulation is done to validate the theoretical analysis.

Research into cutting-edge systems that can satisfy the rising needs for greater data rate and increased reliability is becoming more and more necessary as wireless communication technologies continue to evolve. To enhance the spectral and energy efficiencies, this study examine the performance of massive multiple input multiple output (mMIMO) communication systems using wavelet-order orthogonal frequency division multiplexing (W-OFDM). Also, a communication system was created based on W-OFDM Massive MIMO using MATLAB to model its operation. The simulated system's various stages of operation was examined through detailed analysis and its performance was compare with prior studies and practical networks on OFDM. Key performance metrics, specifically the data throughput, was evaluated to assess the effectiveness and viability of the system. The simulation results indicates that W-OFDM can achieve up to 35% reduction in peak-to-average-power-ratio (PAPR) compared to the traditional OFDM, with a 20% improvement in BER performance under Rayleigh fading conditions. Also, the spectral efficiency of W-OFDM mMIMO outperforms convectional OFDM by 15%, making it a viable technology for future communication networks requiring high data rates and reliability. This implies that, the analytical evaluation confirms that W-OFDM mMIMO provides substantial benefits for improving energy and spectral efficiencies in wireless communication systems, addressing the challenges of increased user demands for high spectrum. This study contributes to the understanding of MIMO systems' performance optimization, highlighting the importance of antenna configuration and cell distance. Comprehensive analysis of spectral efficiency and energy efficiency trade-offs in MIMO systems and identification of optimal antenna configuration for maximum energy efficiency, addressing some of the challenges of future wireless communication systems.

Massive multiple-input–multiple-output (MIMO) systems which include a large number of service antennas have received a great deal of attention as a means to improve both spectral efficiency (SE) and energy efficiency (EE) of wireless communication systems. In this article, we study the feasibility of using Massive MIMO to support a vast amount of underwater Industrial Internet of Things (IIoT) devices. By using a large number of antennas as compensation entities for the low bandwidth in the underwater channel, we show that underwater Massive MIMO can simultaneously support a massive amount of underwater IIoT devices. Electromagnetic radio frequency (RF) and acoustic waves are used to present the performance with respect to SE, throughput (TP) per user equipment (UE), and coverage. Additionally, we show that both Massive MIMO with RF and acoustic waves have the same closed-form upper bounds which can be used as an important design tool of underwater IIoT networks. Numerical results verify the theoretical analysis.

Massive multiple-input multiple-output (MIMO) systems have been drawing considerable interest. In the uplink, massive MIMO systems are commonly studied assuming that each base station (BS) decodes the signals of its user terminals separately and linearly while treating all interference as noise. Although this approach provides improved spectral efficiency (SE) in favorable channel conditions, it is generally sub-optimal from an information-theoretic perspective. In this work we characterize the SE of massive MIMO when the BSs are allowed to jointly decode the received signals. We consider two schemes for handling the interference, and derive their SEs for both finite and asymptotic number of antennas. Simulation tests of the proposed methods illustrate their gains in SE compared to standard separate linear decoding, and show that the standard approach fails to capture the actual achievable rates of massive MIMO systems, particularly when the interference is dominant.

This chapter, the techniques of massive multiple-input-multiple-output (MIMO) and network MIMO are studied. First, massive MIMO is compared with the conventional point-to-point MIMO used in current cellular systems in terms of capacity and energy efficiency. The comparison shows that massive MIMO has huge gains even under the modest conditions using simple linear processing. Then, the implementation of massive MIMO is discussed by focusing on the channel estimation method, the signal detection method and the precoding method. Following this, several main issues in massive MIMO are examined and relevant measures are discussed. Finally, network MIMO is studied by introducing cooperation between base stations to completely remove the inter-cell interference, at the cost of even higher complexity that places limits on the backhaul and fronthaul links.

In wireless communications, massive multiple-input multiple-output (MIMO) systems in which the base station (BS) is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO is an attractive technology for next generation wireless communication systems and for green communications. There are two operation modes: time-division duplex (TDD) and frequency-division duplex (FDD) for massive MIMO systems, and the debate on the two modes is still discussed in the literature. The originator of massive MIMO systems, Marzetta, has presented the basic frame structures for both the TDD and the FDD modes. However, the basic frame structures do not carefully take the hardware impairments such as phase noise (PN) into account. In this paper, by taking the PN as the key hardware impairment into account, we recommend new and efficient frame structures for both the TDD and the FDD modes. The corresponding performance comparison between the two modes is also provided by using the data transmission efficiency (DTE) as the performance measurement. The comparison may help determine which mode should be used for massive MIMO systems.

Energy efficiency is a basic need of the fifth-generation (5G) wireless networks, because the energy sources and capability of the battery are very narrow against the huge demand. Massive MIMO is a great invention of 5G cellular network with a large number of antennas. In this research, the prime aim is to maximize the energy efficiency and spectral efficiency simultaneously of massive MIMO system. Spectral and energy efficiency are formulated with fixed power, number of users, number of antennas with the impact of multiple users and without the impact of multiple users, the ratio of number of antennas and number of users. Trade-off also has been analyzed among the co-located, distributed and hybrid massive MIMO with the Nakagami-m fading channel, which can subtract the co-channel interference in a system and compared with each other. From the simulation results, it is seen that the energy efficiency of distributed massive MIMO is higher than the other two with the variation of fixed power, number of user and the ratio of number of antenna and number of user with respect to spectral efficiency. The energy efficiency of co-located massive MIMO is higher than the other two with the variation of number of antenna.

As a combination of spatial modulation (SM) system and massive multiple-input multiple-output (MIMO) system, massive SM aided MIMO (SM-MIMO) system is recently proposed. In broadband scenarios, single-carrier (SC) modulation is introduced to massive SM-MIMO system, thus massive SC-SM MIMO system is proposed for uplink multi-user transmission over frequency-selective fading channels. In this study, the uplink spectral efficiency (SE) of multi-cell massive SC-SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi-cell massive SC-SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed-form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

The combination of massive multiple-input multiple-output (MIMO) and single-carrier (SC) aided spatial modulation (SM), is recently proposed for uplink multi-user transmission over dispersive channels, which has also attracted plenty of research interest. In this letter, first the spectral efficiency (SE) lower bound with non-uniform power allocation for uplink single-cell massive SC-SM MIMO systems is proposed, in which, the base station (BS) applies maximum ratio combining and many non-ideal factors of the wireless channels are all considered. Then, a barrier function-based gradient projection method is proposed to allocate power of each transmit antenna (TA) in terms of SE maximization. The SE lower bound is shown to be tight via simulation results, and simulation results also show that the SE gain introduced by our proposed PA algorithm is mainly dependent on the TA correlation and number of TAs.

As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI aided RE optimization approach.

In wireless communications, massive multiple-input multiple-output (MIMO) systems in which the base station (BS) is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO is an attractive technology for next-generation wireless communication systems and for green communications. However, phase noise (PN) introduced by the impairment of oscillators can cause a severe performance loss in wireless communication systems. Solving the PN problem for massive MIMO systems in both the downlink and uplink is challenging as it is a multivariate joint PN estimation and data detection problem. The optimal solution is difficult to derive and may lead to high complexity. In this paper, we propose a zero-forcing (ZF) based precoding scheme with PN suppression to solve the PN problem for massive MIMO downlink systems. By using the ideal PN free output signal-to-noise ratio (SNR) of the ZF precoding as a performance upper bound, analytical and numerical results show that when the required SNR is satisfied, the output SNR of the proposed scheme can approach the upper bound, except for very large PN variances. Moreover, the complexity of the proposed scheme is low on both the BS and the user sides, and the BS does not need any feedback information from the users.

This paper investigates the multi-user full-duplex massive multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying system with low-resolution analog-to-digital converts (ADCs), where the zero-forcing transmitting/ zero-forcing receiving (ZFT/ ZFR) precoding scheme is employed. By modeling the loop-interference and quantization noise as additive noise, we perform the analysis of the achievable spectral efficiency (SE) that is given with closed-form expressions. Then, we present the asymptotic analyses under three different power-scaling scenarios. Comparison analysis shows that, with different power-scaling schemes, the system has different ability to restrict the effect of loop interference and low-resolution ADCs. Specially, the impact of loop interference and quantizer errors on achievable SE is restricted effectively only under the power-scaling scenario where the relay's transmit power is scaled down with the number of transmit antennas at relay. At the same time, the effect of loop interference can be also restricted under the case where both the transmit powers at sources and relay are scaled down with the relay's antenna number. The results can be used to guide the design of massive MIMO relaying systems so that the hardware cost and energy consume can be deduced significantly.

As an important part of the channel fading, large scale fading should be considered in downlink massive multiple-input multiple-output (MIMO) systems. This paper investigates the downlink massive MIMO system over a large scale fading channel, when the base station takes advantage of maximum-radio transmission (MRT) precoding. The cases when the base station has perfect and imperfect channel state information (CSI) are considered respectively. Specially, through the analysis of single user's ergodic achievable rate, some novel and approximate expressions for the spectral efficiency (SE) are derived. Based on the approximate SE, the effect of the channel estimation error is analyzed intuitively. In addition, the average SE of all the users with different large-scale fading parameters is carefully investigated. Simulations validate that all the theoretical results coincide with numerical results and the large scale fading factors have little influence on SE reduction resulted from channel estimation.

In this paper, the tradeoff relationship between the spectral efficiency (SE) and energy efficiency (EE) of homogenous cellular networks in which the BSs are arbitrarily distributed is investigated. The network performance metrics of SE and EE are assessed subject to a downlink transmission outage constraint in interference-limited operational environments. The EE is expressed in closed form as a function of SE, based on which the performance bounds of the network are derived. Unlike the traditional inverse relationship between SE and EE, it is found in this paper that there exists an operational regime for which both the SE and EE increase while satisfying the outage requirement, and the density of base stations (BSs) simultaneously sharing the spectrum is optimal. The difference in the performance achieved for the SE when operating in the EE maximizing mode as compared with the SE maximizing mode strongly depends on the received signal-to-interference ratio (SIR) threshold. In the SE-EE tradeoff regime, the analytical tools from microeconomics theory are applied to determine the optimal BS density with respect to the utility achieved by the network operator via balancing the SE and EE objectives. Numerical results show that, by tuning a preference factor toward either the SE or EE metrics, it is feasible to realize Pareto optimal performance.

Recently, unmanned aerial vehicle (UAV) communications have drawn significant research interests. Meanwhile, cell-free (CF) massive multiple-input multiple-output (MIMO) is proposed as a promising technology to achieve the ultimate performance limits. In this paper, we investigate the UAV communication with wireless power transfer (WPT) aided CF massive MIMO systems, where the harvested energy (HE) from the downlink WPT is used to support both uplink data and pilot transmission. Take hardware impairments of UAV into account, novel closed-form downlink HE and uplink spectral efficiency (SE) expressions are derived. UAV communications with small cell (SC) and cellular massive MIMO enabled WPT systems are also considered for comparison. Our results reveal that CF massive MIMO achieves two and four times higher 95%-likely uplink SE than the ones of SC and cellular massive MIMO, respectively. To this end, SE is a concave function of the time-splitting fraction, and the optimal time-splitting fraction for maximizing SE is determined by the altitude and hardware impairment factor of the UAV.