Dynamic Metasurface Antennas for Energy Efficient Uplink Massive MIMO Communications

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.

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).

Dynamic metasurface antennas (DMAs) is a new antenna technology comprising multiple microstrips, each encompassing several reconfigurable metamaterial elements acting as radiating and receiving antennas. The whole unit is usually connected via the input/output port of each microstrip to the radio frequency (RF) chains of the baseband unit. Point-to-point (P2P) multiple-input multiple-output (MIMO) system is one of the most prominent wireless communication systems, with its use cases recently increasing with the booming of machine-to-machine communication. However, it is yet to be explored with DMAs. This work thus studies the achievable rate maximization of a DMA-based P2P MIMO system whose transmitter or receiver is equipped with DMA instead of traditional antennas. Noteworthy, this is the first work to analyze the deployment where both the BS and the user are equipped with DMA. Additionally, we numerically demonstrate how uplink and downlink communication can be achieved in each DMA deployment strategy. Finally, through the simulation results, the performance of our proposed designs is compared with various benchmark systems, such as conventional P2P and IRS-aided MIMO systems, where in both cases, it is shown that the proposed designs provide the best tradeoff in achieving high performance with significant RF chains reduction.

This paper studies the downlink sum-rate and energy efficiency (EE) of massive multiple-input multiple-output (MIMO) systems under the effect of channel aging caused by the movements of users. First, a closed-form expression of the downlink sum-rate where zero-forcing (ZF) precoder is adopted at the base station (BS) is derived. Based on the expression, the power-scaling law that when the number of BS antennas M approaches to infinity, the transmit power of BS can be reduced proportionally to 1/√M is still satisfied in the massive MIMO downlink system. Second, we utilize a general power consumption model to analyze the EE of the system. Both theoretical analysis and Monte-Carlo simulations show that the emergence of channel aging could degrade the sum-rate and the EE of the system.

The performance of the hybrid beamforming system based on the full-connected structure is close to that of the full-digital one in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, due to the high complexity of circuit wiring in practical applications, it has been greatly restricted. This paper investigates the hybrid (analog/digital) beamforming design of a multiuser mmWave massive MIMO system with sub-connected structure. For this system, we focus on a sum-rate maximization probelm. Considering the joint design of the receiver, and transmitter, a two-stage design method is adopted. In the analog stage, the analog beamformer, and combiner are designed by the proposed piecewise dual joint iterative approximation (PDJIA) method, which not only can obtain closed-form solutions but also has linear property. Based on the criterion of avoiding the loss of information, the problem of digital beamforming is solved by exploiting the baseband piecewise successive approximation (BPSA) method, which can effectively increase the number of users served. Simulation results show that the proposed scheme outperforms the state-of-the art MIMO hybrid beamforming design schemes, and more accurately approximates to that of the full-digital system.

This paper presents multiple-input multiple-output (MIMO) measurements in the 20-GHz band, and evaluates the basic performance of massive MIMO systems when considering 100 elements at a base station (BS) and seven user equipment units. First, it is shown that zero forcing (ZF) is essential as a transmission scheme in massive MIMO systems, whereas the maximum-ratio combining method cannot reduce interference effectively in such systems. In addition, it is shown that analog–digital hybrid beamforming can be used to reduce the calculation complexity and improve hardware implementation. Next, we compared the Shannon capacity with the achievable bit rate (ABR), using ZF for seven-user MIMO transmissions with 100 elements at the BS. When the number of degrees of freedom in the array antenna is guaranteed, the ABR is almost the same as the Shannon capacity. Moreover, multiuser transmission is realized by linear decoding. Thus, decoding methods such as ZF are effective in massive MIMO systems.

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.

Massive multiple-input multiple-output (MIMO) systems are designed to increase data capacity systems. However, there are growing social and economic concerns in developing these communication technologies. Energy efficient systems are desirable for these systems considering the costs and minimizing resources. Thus, the optimization of these approaches is significant in the advancement of green communication. This paper converges on the circuit power in modelling massive MIMO systems. Specifically, to apply various power consumption of the transceiver chains, to determine the desired power of the base station value for maximum energy efficiency, and to define the effects of a realistic circuit power consumption model in Massive MIMO systems. Mathematical models were developed, and the corresponding codes were simulated using MATLAB. Different processing schemes were also studied, namely, maximum ratio transmission/maximum ratio combining, and zero forcing for the perfect and imperfect single-cells scenario. In contrast, multiple cells consider different re-use factors. As observed, a low power wattage consumption of transceiver chains and base stations is desired to produce a more remarkable relative change in energy efficiency. Local oscillators' power usage is also recommended to be low wattage. Low-power systems utilize low energy consumption but not disregarding higher energy efficiency. Other factors in data throughput – bandwidth, transmit power, noise power spectral density, and path loss, can be studied to optimize energy-efficient massive MIMO systems.

To meet up with the ever increasing subscribers' demand for higher data rates and mobile data traffic growth in the telecommunication industry, the fifth generation (5G) systems is being considered for the next future cellular communication standards. The two principal design requirements being aimed at in 5G are robust data transmission rates in Gigabits and low power consumption systems. Massive multiple input multiple output (M-MIMO) technology is an evolving smart antenna technology which has some key promising potentials to boost 5G networks in meeting the aforementioned requirements. However, there is an emergent concern that increased number of antenna arrays in M-MIMO system could induce high power consumption and poor energy efficiency when deployed at the base stations (BSs). Also, inter-cellular interference which occurs as a result of pilot contamination, fast fading and uncorrelated noise effects in the radio channels are other open issues in M-MIMO system. This work investigates and compare the achievable sum rates and energy efficiency of a downlink single cell M-MIMO systems utilizing linear and nonlinear precoding schemes. First, we have shown how the increasing signal-to-noise ratio and M-antennas impact the achievable sum rates. Furthermore, the energy saving potentials of M-MIMO systems in macro, micro and pico cellular environments when linear and nonlinear precoding schemes are utilized at the BS have been demonstrated. Particularly, by means of power fairness index, the tradeoff among the energy efficiency, sum rate and the system users have also been presented and discussed. Results show that substantial energy efficiency improvements can be achieved in micro and pico cellular environments of downlink M-MIMO systems when non-linear successive interference cancellation precoding is applied compared to linear precoding schemes.

It is predicted that there will be billions of machine type communication (MTC) devices to be deployed in near future. This will certainly cause severe access congestion and system overload which is one of the major challenges for the proper operation of 5G networks. Adopting device-to-device (D2D) communications into massive multiple-input multiple-output (MIMO) systems has been considered as a potential solution to alleviate the overload of MTC devices by offloading the MTC traffic onto D2D links. This work proposes a novel pilot reuse (PR) and power control (PC) for energy efficiency (EE) optimization of the uplink D2D underlaying massive MIMO cellular systems. Although the use of large scale antenna array at the base station (BS) can eliminate most of the D2D-to-Cellular interference, the Cellular-to-D2D interference and the channel estimation error caused by PR will remain significant. Motivated by this, and in order to reduce the channel estimation error, in this paper a novel heuristic PR optimum pilot reuse scheme is proposed for D2D transmitters (D2DTs) selection. By taking into account the interference among users as well as the overall power consumption, the overall system EE is maximized through power optimization while maintaining the quality-of-service (QoS) provisions for both cellular users (CUEs) and D2D pairs. The power optimization problem is modeled as a non-cooperative game and, as such, a distributed iterative power control algorithm which optimizes users power sequentially is proposed. Various performance evaluation results obtained by means of computer simulations have shown that the proposed PR scheme and PC algorithm can significantly increase the overall system EE.

Hybrid analog and digital beamforming has emerged as a key enabling technology for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems since it can balance the trade-off between system performance and hardware efficiency. Owing to the strong ability of central control, cooperative networks show great potential to enhance the spectral efficiency of mmWave communications. In this paper, we consider cooperative mmWave MIMO systems and propose user association and hybrid beamforming design algorithms for three typical hybrid beamforming architectures. The central processing unit (CPU) of the cooperative networks first matches the service pairs of base stations (BSs) and users. Then, an iterative hybrid beamforming design algorithm is proposed to maximize the weighted achievable sum-rate performance of the mmWave MIMO system with fully connected hybrid beamforming architecture. Moreover, a heuristic analog beamforming design algorithm is introduced for the fixed subarray hybrid beamforming architecture. In an effort to further exploit multiple-antenna diversities, we also consider the dynamic subarray architecture and propose a novel antenna design algorithm for the analog beamforming design. Simulation results illustrate that the proposed hybrid beamforming algorithms achieve a significant performance improvement than other existing approaches and the dynamic subarray architecture has great advantages of improving the energy efficiency (EE) performance.

The sum-rate of the uplink multiuser massive multiple-input multiple-output (MIMO) system is investigated. For the considered massive MIMO systems, the base station (BS) is equipped with a large number of antennas, which are assembled into several sub-arrays. Meanwhile, in each of the sub-arrays, antennas are densely installed. With the analysis of the sum-rate, we find that, for given total number of antennas, the sum-rate achieved by the considered systems improves, when the number of sub-arrays increases. However, the improvement of the achievable sum-rate is marginal, when the number of sub-arrays keeps on increasing. Furthermore, when the system is lightly loaded, even when all of antennas are assembled into a single compact antenna array, the achievable sum-rate is very close to that when all antennas are independent.

An optimization framework for RIS-based energy-efficient multi-cell NOMA systems

Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising technology for providing uniformly good services in future networks. The aim of this paper is to provide the first comparison of the uplink performance of cell-free massive MIMO systems with low-resolution analogto-digital converters under the zero-forcing (ZF) and maximum ratio combining (MRC) receivers. By utilizing an addictive quantization model, a novel closed-form uplink spectral efficiency (SE) expression is derived for the ZF receiver. Moreover, the energy efficiency (EE) of the considered system is analyzed. In contrast to previous works, it turns out that the ZF receiver outperforms the MRC receiver with a large number of quantization bits in terms of SE and EE. Furthermore, it is interesting to find that a better EE-SE trade-off can be achieved by employing a large number of antennas at the access point. Finally, pilot assignment schemes are used to minimize the pilot contamination and a maxmin power control scheme is proposed to improve both SE and EE performance.

Multiple Input Multiple Output (MIMO) low complexity receiver which utilizes the compressive sensing detection for Spatial Modulation in large scale MIMO system in order to reduce the system complexity. In conventional MIMO system; huge amount of antennas is used at both ends to exploit the multipath propagation. This system maximizes the throughput performance and data rates are increased but only at the cost of high hardware complexity and increased power-consumption. Spatial Modulation Matching Pursuit (SMMP) is the proposed enhanced CS technique used for the improvement of detection performance. Hence, this paper reviews recent research findings concerning normalized Compressive Sensing (CS) detection algorithm, used for Spatial Modulation (SM) in massive MIMO, to lowers the signal processing complexity, which in result improves the energy efficiency of system against that of conventional MIMO system.This strategy is achieved by involving additional structures and sparsity in which a single transmitter antenna or a subset of it is turned on at each case to transmit a certain data. The subset of antenna which is turned on for transmission depends on approaching data bits. Therefore, the total increase in the spectral efficiency of the system is given as base-two logarithm of whole antennas at the transmitter. It reduces the signal processing load at base station and doesn’t depend upon any synchronization between transmitters. The Spatial Modulation Matching Pursuit used prevents the Inter Channel Interference (ICI) of the system which in result improves the Bit Error Rate (BER) performance than the typical MIMO system.