Downlink Massive MIMO Systems: Achievable Sum Rates and Energy Efficiency Perspective for Future 5G Systems

In this paper, an energy efficient precoding scheme is proposed for massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme outperforms the conventional zero forcing (ZF) precoder in terms of the power consumption and the achievable transmission rate by including a complex valued regularization parameter such that the energy efficiency is improved. The proposed energy efficient complex regularized zero forcing precoder is analytically evaluated, verified by simulations and compared to the ZF precoder. The obtained results confirm the robustness of the proposed precoder in massive MIMO systems.

Full-duplex (FD) transmission holds a great potential of improving the sum data rate of wireless communication systems. However, the self-interference introduced by the full-duplex transmitter brings a big challenge to enhance the energy efficiency. This paper investigates the power allocation problem in a full-duplex two-way (FDTW) communication network over an OFDM channel, aiming at improving the sum data rate and energy efficiency. We first characterize the sum rate and energy efficiency achieved in a single-carrier FDTW system. The optimal transmit power that achieves the maximal sum data rate is presented. The energy efficiency maximization problem is solved by using fractional programming. Then we further formulate sum rate and energy efficiency maximization problem in a multi-subcarrier FDTW system. In particular, the sub-optimal transmit power allocation which achieves a decent sum rate improvement is found by using a proposed iterative algorithm. By combining the iterative algorithm and fractional programming, we further maximize the energy efficiency of the multi-subcarrier system. With our proposed algorithm, we can easily obtain an optimal transmit power that approximates the global optimal solution. Simulation results show that using the obtained optimal transmit power allocation algorithm can significantly improve the sum rate and energy efficiency in both single-carrier and multi-subcarrier systems.

In this paper, we investigate the coexistence of two technologies that have been put forward for the fifth generation (5G) of cellular networks, namely, network-assisted device-to-device (D2D) communications and massive MIMO (multiple-input multiple-output). Potential benefits of both technologies are known individually, but the tradeoffs resulting from their coexistence have not been adequately addressed. To this end, we assume that D2D users reuse the downlink resources of cellular networks in an underlay fashion. In addition, multiple antennas at the BS are used in order to obtain precoding gains and simultaneously support multiple cellular users using multiuser or massive MIMO technique. Two metrics are considered, namely the average sum rate (ASR) and energy efficiency (EE). We derive tractable and directly computable expressions and study the tradeoffs between the ASR and EE as functions of the number of BS antennas, the number of cellular users and the density of D2D users within a given coverage area. Our results show that both the ASR and EE behave differently in scenarios with low and high density of D2D users, and that coexistence of underlay D2D communications and massive MIMO is mainly beneficial in low densities of D2D users.

As an effective technology for boosting the performance of wireless communications, massive multiple-input multiple-output (MIMO) systems based on symmetric antenna arrays have been extensively studied. Using low-resolution analog-to-digital converters (ADCs) at the receiver can greatly reduce hardware costs and circuit complexity to further improve the energy efficiency (EE) of the system. There are significant research on the design of MIMO detectors but there is limited study on their performance in terms of EE. This paper studies the effect of signal detection on the EE in practical systems, and proposes to apply several signal detectors based on lattice reduction successive interference cancellation (LR-SIC) to massive MIMO systems with low-precision ADCs. We report results on their achievable EE in fading environments with typical modeling of the path loss and detailed analysis of the power consumption of the transceiver circuits. It is shown that the EE-optimal solution depends highly on the application scenarios, e.g., the number of antennas employed, the cell size, and the signal processing efficiency. Consequently, the signal detector must be properly selected according to the application scenario to maximize the system EE. In addition, medium-resolution ADCs should be selected to balance their own power consumption and the associated nonlinear distortion to maximize the EE of system.

A new graph modulation scheme is proposed for massive multiple-input multiple-output (MIMO) systems with a large number of transmit antennas. Each data symbol is transmitted in the form of multiple weighted replicas. The replicas from different symbols are multiplexed onto a two-dimensional (2D) space-time grid, which is formed by multiple transmit antennas in the space domain and different transmission instants in the time domain. Multiplexing repeated symbols onto a 2D space-time grid achieves performance gains in terms of both diversity gain and high degree-of-freedom of the received signals. In addition, it enables a bipartite graph representation of the system, with one set of nodes being the transmitted symbols and the other set containing the received space-time samples. A modified message passing algorithm is proposed to iteratively detect the transmitted symbols by exploiting the structure of the bipartite graph. The proposed graph modulation scheme can flexibly adjust the tradeoff among energy efficiency, spectral efficiency, space diversity, and detection complexity by choosing different design parameters based on application requirements. Simulation results demonstrate that the proposed approach can achieve significant performance gains over the conventional spatial multiplexing scheme used in MIMO systems.

Massive multiple-input multiple-output (MIMO) technology for wireless communication is considered a key technology for the future fifth-generation (5G) communication networks. To reduce the propagation loss of channel, MIMO systems with analog or digital hybrid precoders and combiners (HPC) architecture have been used. This review paper presents the various types of analog and digital HPC designs for the massive MIMO communication systems that were investigated within the last decade. The reviewed systems are useful for both single-user (SU) and multi-user MIMO (MU-MIMO) systems. The key parameter of the study was spectral efficiency (SE), signal-to-noise ratio (SNR), signal-to-interference pulse noise ratio (SINR), system sum-rate (SR) performance, computational complexity (CC), co-channel interference (CCI), inter-primary base station interference (IPBSI), multiuser interference (MUI), effective desired channel gain (EDCG), and minimum mean square error (MMSE).

The optimum transmission strategy for maximizing energy efficiency (EE) of a multi-user massive multiple-input multiple-output (MIMO) system in radio frequency energy harvesting networks is investigated. We focus on dynamic time-switching (TS) antennas, to avoid the practical problems of power-splitting antennas, such as complex architectures, power loss and signal distortion when splitting the power of the received signal into power for information decoding (ID) and energy harvesting (EH). However, since a single TS antenna cannot serve ID and EH simultaneously, the MIMO system is considered in this paper. We thus formulate an EE optimization problem and propose an iterative algorithm as a tractable solution, including an antenna selection strategy to optimally switch each TS antenna between ID mode and EH mode using nonlinear fractional programming and the Lagrange dual method. Further, the problem is solved under practical constraints of maximum transmission power and outage probabilities for a minimum amount of harvested power and rate capacity for each user. Simulation results show that the proposed algorithm is more energy-efficient than that of baseline schemes, and demonstrates the trade-off between the required amount of harvested power and energy efficiency.

In this paper, the performance of an RF energy harvesting scheme for multi-user massive multiple-input multiple-output (MIMO) is investigated in the presence of multiple active jammers. The key idea is to exploit the jamming transmissions as an energy source to be harvested at the legitimate users. To this end, the achievable uplink sum rate expressions are derived in closed-form for two different antenna configurations. An optimal time-switching policy is also proposed to ensure user-fairness in terms of both harvested energy and achievable rate. Besides, the essential trade-off between the harvested energy and achievable sum rate are quantified in closed-form. Our analysis reveals that the massive MIMO systems can make use of RF signals of the jamming attacks for boosting the amount of harvested energy at the served users. Numerical results illustrate the effectiveness of the derived closed-form expressions over Monte-Carlo simulations.

We investigate the achievable sum rate and energy efficiency of zero-forcing precoded downlink massive multipleinput multiple-output systems in Ricean fading channels. A simple and accurate approximation of the average sum rate is presented, which is valid for a system with arbitrary rank channel means. Based on this expression, the optimal power allocation strategy maximizing the average sum rate is derived. Moreover, considering a general power consumption model, the energy efficiency of the system with rank-1 channel means is characterized. Specifically, the impact of key system parameters, such as the number of users N, the number of BS antennas M, Ricean factor K and the signal-to-noise ratio (SNR) ρ are studied, and closed-form expressions for the optimal ρ and M maximizing the energy efficiency are derived. Our findings show that the optimal power allocation scheme follows the water filling principle, and it can substantially enhance the average sum rate in the presence of strong line-of-sight effect in the low SNR regime. In addition, we demonstrate that the Ricean factor K has significant impact on the optimal values of M, N and ρ.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system is a key technology of 5G communications and a research focus of 6G communications in the future. It can be widely used in communication, radar, power and transmission systems. In the mmWave MIMO system, hybrid beamforming (HBF) is regarded as a potentially promising technology, because it keeps a good balance between hardware complexity and system performance. In this paper, we propose a new hybrid beamforming scheme for the multi-user MIMO (MU-MIMO) scenario. We first design the analog beamforming by trying to avoid the loss of information to maximize the sum-rate of the communication system. Then based on the idea of successive interference cancellation (SIC), we develop the successive block diagonalization (SBD) method to eliminate interference between two adjacent users, which can avoid the computation caused by the influence between users that are far apart. Unlike the baseband BD technology, the proposed scheme has low complexity. The simulation results are shown to validate the proposed scheme.

In this paper, we investigate the achievable sum rate and energy efficiency of downlink massive multiple-input multiple- output antenna systems with zero-forcing precoding, by taking into account the randomness of user locations. Specifically, we propose two types of non-uniform user distributions, namely, center-intensive user distribution and edge-intensive user distribution. Based on these user distributions, we derive novel tight lower and upper bounds on the average sum rate. In addition, the impact of user distributions on the optimal number of users maximizing the sum rate is characterized. Moreover, by adopting a realistic power consumption model which accounts for the transmit power, circuit power and signal processing power, the energy efficiency of the sys- tem is studied. In particular, closed-form solutions for the key system parameters, such as the number of antennas and the optimal transmit signal-to-noise ratio maximizing the energy efficiency, are obtained. The findings of the paper suggest that user distribution has a significant impact on the system performance: for instance, the highest average sum rate is achieved with the center-intensive user distribution, while the lowest average sum rate is obtained with the edge-intensive user distribution. Also, more users can be served with the center-intensive user distribution.

The development of massive multiple-input multiple-output (MaMIMO) techniques is motivated by the requirements of large spectral efficiency and reduced power consumption. The implementation of a large number of antennas offers a large spectral efficiency and link reliability. Moreover, the use of MaMIMO allows scaling down the transmitted power proportionally to the number of antennas used, which may lead to a significant improvement in terms of energy efficiency. Orthogonal frequency division multiplexing (OFDM) in combination with MaMIMO has a considerable potential to obtain very high data rates and high quality of service (QoS). However, MaMIMO systems require a mobile equipped with multiple antennas at the transmitter. This is a challenging issue in mobile devices mostly due to their size, cost, and computing power limitations. In MaMIMO, the radiated power per antenna decreases linearly with the number of antennas. Moreover, the effects of small-scale fading, non-coherent interference, and receiver noise are minimized. However, a massive number of antennas require a separate transceiver chain and power amplifier (PA) for each antenna (unless analog or hybrid analog-digital structures are used for beamforming purposes, in which case the number of RF chains can be reduced). In this situation, the size and costs of the analog front-end become a critical issue. The cost and size optimization implies the use of low-cost components which increase the imperfections that degrade the system performance. In this chapter, MaMIMO system performance, considering front-end RF imperfections and ADC/DAC with limited resolution, is carefully studied. Spectral and energy efficiency for the uplink and downlink scenarios are evaluated to quantify the overall system performance. Finally, low-resolution precoding techniques, antenna coupling, channel non-reciprocity, and channel estimation errors are also addressed.

Massive multiple-input multiple-output (MIMO) systems are expected to achieve substantial gains in frequency efficiency compared to conventional MIMO systems. However, these promising gains are limited by the pilot contamination in multicell scenario, which results from reusing the same set of pilot sequences in neighboring cells. This paper proposes a clustering based pilot allocation (CPA) algorithm to mitigate the effects of pilot contamination. Specifically, we first define the inter-cell interference (ICI) relationship between users, based on which the users are divided into high and low ICI groups. Then, the users in low ICI group reuse pilots randomly, while the users in high ICI group are clustered by affinity propagation (AP) clustering method. Finally, we perform different pilot allocation strategies according to the types of user clusters. Simulation results show that the proposed algorithm can significantly improve the pilot efficiency compared to the existing pilot allocation algorithms using grouping ideas.

Minimum mean square error hometd has proved its superiority for signal detection in massive multiple-input multiple-output (MIMO) systems for its near-optimal performance. However, the detection efficiency is restrained by a high computation complexity and low parallelism operation of matrix inversion. This paper presented a hardware efficient signal detector based on low complexity Lanczos Method, which replaced the matrix inversion with a series iterations of linear equation and reduce the computational complexity form O(N T 3) to O(N T 2) where N T denotes the number of users. According to the algorithm, a signal detector for 128×8 MIMO system is designed and implemented using TSMC 65nm CMOS technology. The results are 0.441 Mbps/mW (throughput/power) and 206.55 Mbps/mm2 (throughput/area), which are increased by 2.4× and 1.82× that of recently proposed Neumman method.

In this paper, we investigate the suitability of single carrier frequency domain processing (SC-FDP) as the downlink transmission scheme in a massive multiple-input multiple-output (MIMO) system. By deriving the sum-rate of the SC-FDP massive MIMO system theoretically, we show that this method obtains a sum-rate similar to that of orthogonal frequency division multiplexing (OFDM) massive MIMO. We also derive the theoretical sum-rate of both SC-FDP and OFDM in a non-synchronized massive MIMO scenario and show that the rate of the former is significantly larger than that of the latter. Moreover, we theoretically analyze the sum-rate of both systems in the presence of power amplifier non-linearity. All the sum-rates are derived for both zero forcing and matched filter precoding schemes. The results show that the effect of power amplifier non-linearity on the sum-rate of both systems is similar when the number of users is large. We also compare SC-FDP with OFDM from the peak to average power ratio (PAPR) and complexity viewpoints. Although the PAPR of SC-FDP signals is lower than that of OFDM signals, for MIMO systems, the difference between their PAPR decreases as we increase the number of users. Thus, both techniques can have similar PAPR in massive MIMO systems. The overall complexity of SC-FDP and OFDM is similar. Due to the mentioned facts, SC-FDP can be considered as a promising transmission scheme for the downlink of the massive MIMO systems in the presence of carrier frequency offset and power amplifier non-linearities.