Energy-efficient Hybrid Precoding Research Articles

Energy Efficient Hybrid Precoding for Adaptive Partially-Connected mmWave Massive MIMO: A Decomposition-Based Approach

Energy Efficient Hybrid Precoding for Adaptive Partially-Connected mmWave Massive MIMO: A Decomposition-Based Approach

AFC-CE Hybrid Precoding for Energy-Efficient mmWave MIMO Systems

In this letter, an energy-efficient hybrid precoding method based on cross-entropy (CE) is proposed for the adaptive fully-connected (AFC) structure in millimeter wave massive multiple-input multiple-output systems, named the AFC-CE algorithm. By the nonlinear Tomlinson-Harashima precoding method, the non-convex spectrum efficiency maximization problem is transformed into a matrix residual minimization problem between the hybrid precoding matrix and the optimal precoding matrix. Then, using CE theory, the matrix residuals minimization problem is transformed into a probability distribution optimization problem of switching precoding matrix elements. Finally, the probability distribution parameters are updated adaptively by minimizing the CE between the estimated value and the expected value, until the residual converges and the switching precoding matrix is obtained. On this basis, the generalized power method and the least square method are used to give the phase shifts precoding matrix and the digital precoding matrix, respectively. Theoretical analysis and simulation results verify that the proposed method not only has lower computational complexity but also can provide better spectral efficiency and energy efficiency.

A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

Millimeter-Wave (mmWave) Massive MIMO is one of the most effective technology for the fifth-generation (5G) wireless networks. It improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base station. However, increasing the number of antennas requires a large number of radio frequency (RF) chains which results in high power consumption. In order to reduce the RF chain's energy, cost and provide desirable quality-of-service (QoS) to the subscribers, this paper proposes an energy-efficient hybrid precoding algorithm for mmWave massive MIMO networks based on the idea of RF chains selection. The sparse digital precoding problem is generated by utilizing the analog precoding codebook. Then, it is jointly solved through iterative fractional programming and successive convex optimization (SCA) techniques. Simulation results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.

Hybrid Precoding Algorithm for Millimeter-Wave Massive MIMO Systems with Subconnection Structures

In mmWave massive MIMO systems, traditional digital precoding is difficult to be implemented because of the high cost and energy consumption of RF chains. Fortunately, the hybrid precoding which combines digital precoding and analog precoding not only solves this problem successfully, but also improves the performance of the system effectively. However, due to the constant mode constraint introduced by the phase shifter in the analog domain, it is difficult to solve the hybrid precoding directly. There is a solution which divides the total optimization problem into two stages to solve, that is, first fix the digital precoding matrix, solve the analog precoding matrix, and then optimize the digital precoding matrix according to the obtained analog precoding matrix. In this paper, a high energy-efficient hybrid precoding scheme is proposed for the subconnection structure. In the first stage, the optimization problem can be decomposed into a series of subproblems by means of the independent submatrix structure of the analog precoding matrix. When the optimized analog precoding matrix is obtained, the digital precoding matrix can be solved by the minimum mean error (MMSE). Finally, the digital precoding matrix is normalized to satisfy the constraint conditions. The simulation results demonstrate that the performance of the proposed algorithm is close to that of fully digital precoding based on subconnection structure and better than that of the existing algorithms. In addition, this paper presents the simulation analysis of the algorithm performance under imperfect channel state information. Simulation results show that when the estimation accuracy of channel state information is 0.8, the spectral efficiency of the proposed algorithm can already be maintained at a good level.

Hierarchical Energy Efficient Hybrid Precoding for Configurable Sub-Connected MIMO Systems

This paper proposed a configurable sub-connected architecture with a framework that dynamically activates the near-optimal subset of antennas and RF chains to implement energy-efficient hybrid precoding in millimeter wave multiple-input multiple-output system. Since the exhaust search is computational intractable, we propose a two-stage hybrid precoding algorithm, where the digital precoder is designed to eliminate the inter-user interference by zero-forcing rule. Specifically, in the first stage, we introduce an extended cross-entropy algorithm that adaptively updates the probability distribution of potential states in the analog precoder matrix, which can generate a solution that is close to the optimal with a sufficiently high probability. In the second stage, a QR-based subset selection algorithm is proposed to pick the near-optimal subset of the RF chains to further cut down on the energy cost. Simulation results show that proposed extend-CE algorithm gets favorable performance in terms of energy efficiency, and QR-based RF chain selection can achieve a near-optimal performance. Other hybrid precoding algorithms can also be incorporated into the proposed RF chain selection algorithm.

Energy-Efficient Hybrid Precoding for mmWave MIMO Systems With Phase Modulation Array

The hybrid precoding technique targeted at reducing the number of RF chains without obvious performance loss becomes a promising candidate in 5G communication. In this paper, an energy efficient hybrid precoding is proposed for the mmWave MIMO systems, in which phase modulation array (PMA) based analog precoding is adopted instead of phase shifters to reduce the power consumption and improve the energy efficiency. Specifically, PMA, consisting of delay lines and switches, enables to control phase and amplitude simultaneously, thus imposes additional constraint on the analog precoding. On this basis, the hybrid precoding problem is formulated for the PMA-based mmWave systems. Furthermore, an enhanced orthogonal constraint algorithm and an alternating optimization algorithm are proposed for the full-connected and sub-connected hybrid precoders, respectively. In addition, a more realistic situation, that there exists a minimum switch-on duration when the RF switch turns on, is considered for the proposed PMA-based hybrid precoder. Finally, simulation results have verified that, the PMA-based precoding can achieve higher spectral efficiency and energy efficiency when compared with the phase shifters based counterparts. More importantly, the proposed PMA-based hybrid precoder can significantly reduce the number of radio frequency chains, resulting in low energy consumption for its application in 5G green communication.

Energy-Efficient Hybrid Precoding Design for Integrated Multicast-Unicast Millimeter Wave Communications With SWIPT

In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes.

Machine Learning Inspired Hybrid Precoding for Wideband Millimeter-Wave Massive MIMO Systems

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) has already been considered as a promising solution to meet the requirement of the higher data rate for the future Internet of Things (IoTs). Hybrid precoding is an effective solution for the mmWave massive MIMO systems to significantly decrease the number of radio frequency (RF) chains without an apparent sum-rate loss. However, the current literature on hybrid precoding considers either the high-resolution (HR) phase shifters (PSs) with high power consumption or the impractical narrowband mmWave channel model. To this end, in this paper, we investigate an energy-efficient hybrid precoding scheme using one-bit PSs for practical frequency-selective wideband mmWave massive MIMO systems. Specifically, we provide the energy consumption analysis to reveal the fact that the energy consumed by the one-bit PSs is much lower than that by the HR-PSs, and the array gain loss incurred by using one-bit PSs is minimal. Moreover, motivated by the cross-entropy optimization (CEO) algorithm evolved for machine learning, we propose the CEO-based hybrid precoding scheme to maximize the achievable sum-rate of the considered system. In the CEO-based hybrid precoding, we update the probability distributions of the elements in the hybrid precoder to minimize the cross-entropy between the two probability distributions so that we can generate the final solution close to the optimal one. Furthermore, we extend the proposed CEO-based hybrid precoding scheme from the case with one-bit PSs to the general case with HR-PSs to show that our solution can also be applied in other scenarios. The performance evaluation demonstrates that our proposed scheme can obtain near-optimal sum-rate and considerably higher energy efficiency than some existing solutions.

Energy-Efficient Hybrid Precoding Design for Millimeter-Wave Massive MIMO Systems Via Coordinate Update Algorithms

This paper considers an energy-efficient, hybrid digital, and analog precoding design problem in millimeter-wave massive multiple-input multiple-output systems, in which the analog precoder is realized with a small number of energy-efficient switches and inverters rather than with a large number of high-resolution phase shifters. However, finding the optimal weights of such energy-efficient hybrid precoding requires solving a challenging combinatorial problem. A known solver, namely “adaptive cross-entropy (ACE),” can provide a reasonable solution but its computational complexity is still high. Thus, a simple yet effective coordinate update algorithm (CUA) is proposed in this paper to reduce the computational complexity while maintaining and even improving the achievable rate performance. In addition, we propose a new hybrid precoding architecture that introduces the antenna selection mechanism into the conventional energy-efficient-based hybrid precoding architecture to further reduce energy consumption. Although, the resulting design problem of this new architecture becomes more complex in that the traditional ACE-based solver cannot be applied directly, the proposed CUA-based solver can still handle this problem. The simulation results demonstrate that the proposed CUA-based solver provides the same or even an improved achievable rate performance than the ACE-based solver at a lower complexity. Furthermore, the energy consumption of the proposed hybrid precoding architecture is lower than that of the conventional energy-efficient hybrid precoding architecture.

Energy Efficiency Optimization of 5G Radio Frequency Chain Systems

With the massive multi-input multi-output (MIMO) antennas technology adopted for the fifth generation (5G) wireless communication systems, a large number of radio frequency (RF) chains have to be employed for RF circuits. However, a large number of RF chains not only increase the cost of RF circuits but also consume additional energy in 5G wireless communication systems. In this paper we investigate energy and cost efficiency optimization solutions for 5G wireless communication systems with a large number of antennas and RF chains. An energy efficiency optimization problem is formulated for 5G wireless communication systems using massive MIMO antennas and millimeter wave technology. Considering the nonconcave feature of the objective function, a suboptimal iterative algorithm, i.e., the energy efficient hybrid precoding (EEHP) algorithm is developed for maximizing the energy efficiency of 5G wireless communication systems. To reduce the cost of RF circuits, the energy efficient hybrid precoding with the minimum number of RF chains (EEHP-MRFC) algorithm is also proposed. Moreover, the critical number of antennas searching (CNAS) and user equipment number optimization (UENO) algorithms are further developed to optimize the energy efficiency of 5G wireless communication systems by the number of transmit antennas and UEs. Compared with the maximum energy efficiency of conventional zero-forcing (ZF) precoding algorithm, numerical results indicate that the maximum energy efficiency of the proposed EEHP and EEHP-MRFC algorithms are improved by 220% and 171%, respectively.

Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to avoid energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully-connected architecture requiring a large number of phase shifters, which is also energyintensive. In this paper, we focus on the more energy-efficient hybrid precoding with sub-connected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multi-user signal detection, we first propose to decompose the total achievable rate optimization problem with non-convex constraints into a series of simple sub-rate optimization problems, each of which only considers one sub-antenna array. Then, we prove that maximizing the achievable sub-rate of each sub-antenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SICbased hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify the near-optimal performance of SIC-based hybrid precoding.