**1. Introduction**

The use of millimeter-wave (mmWave) communications has become increasingly popular as a potential solution for current and upcoming cellular systems, mainly due to the extensive yet underutilized mmWave frequency range [1]. To ensure an adequate link margin and achieve array gain, massive multiple-input multiple-output (MIMO) antenna arrays are required for mmWave systems [2]. The use of traditional analog precoding and combining schemes in mmWave MIMO systems is not practical due to the high hardware cost and power consumption of the radio frequency (RF)

chains. In light of this, hybrid precoding and combining schemes are viewed as a promising technology that can strike a balance between system performance and hardware complexity. There are two primary hybrid precoding and combining architectures used in millimeter-wave systems: full array (FA) [2–15] and subarray (SA) [16–23] architectures. The FA architecture is commonly employed in hybrid precoding and combining systems. With this architecture, phase shifters (PSs) connect each RF chain to each antenna, leading to a linear increase in the number of PSs with the number of antennas. In contrast, the SA architecture connects each RF chain to a subset of antennas, requiring fewer PSs than the FA architecture.

In the literature, FA hybrid architecture for mmWave systems has received significant attention. The authors of [2] proposed a hybrid precoding/combining algorithm based on simultaneous orthogonal matching pursuit, achieving performance comparable to that of optimal digital beamforming with high complexity. In [4], we introduced an iterative low-complexity hybrid design algorithm based on gradient descent. The work in [5] proposed hybrid designs for Mini-Mental State Examination (MMSE)-based rate balancing in mmWave multiuser MIMO systems and the work in [7] proposed joint hybrid precoding and combining for massive MIMO systems. In [8], a greedy approach is introduced without assumptions about channel structure or array geometry. The work in [9] presented the hybrid design by alternating minimization (HD-AM) algorithm, which achieves high spectral efficiency but is limited to equal numbers of data streams and RF chains. Manifold optimization-based hybrid precoding algorithm in [10] achieves high spectral efficiency but with high computation complexity. Sohrabi and Yu in [11] proposed a heuristic hybrid beamforming algorithm, while the authors of [12, 13] developed gradient projection algorithms for hybrid beamforming design. In multiuser scenarios [14, 15], digital beamforming removes interuser interference, and the analog precoders and combiners maximize user signal power.

Although FA hybrid architecture led to the lower complexity of hybrid precoding and combining algorithms compared to the analog one, the high cost, power consumption, and hardware complexity of this architecture persist due to the need for a phase shifter (PS) to connect each RF chain to every antenna [16, 17]. To address these challenges, the SA architecture has gained popularity as a practical solution for hybrid precoding and combining designs that offer a balance between performance, complexity, and cost. SA architectures for hybrid precoding can be classified as fixed SA [16–18], adaptive SA [19], and dynamic SA [20, 21]. In fixed SA, each RF chain is connected to a subarray of antennas, while switches are used in dynamic SA. Dynamic SA achieves similar performance as FA with high complexity as compared to the fixed SA. Overlapped SA architecture with hybrid precoding can improve the spectral efficiency of the SA architecture and still lower the complexity compared to the FA architecture [18]. A study in [16] presented an energy-efficient hybrid precoding technique for the fixed SA architecture. The technique utilized successive interference cancelation and assumed a diagonal digital precoder with real elements. Two lowcomplexity hybrid precoding algorithms for mmWave MIMO systems with fixed SA architecture were proposed and studied in [17]. In [18], we proposed and highlighted the use of overlapped SA architecture for improved spectral efficiency. An adaptive hybrid precoding approach for SA architecture was studied in [19]. Dynamic SA architectures in [20, 21] provided higher spectral efficiency but with increased complexity. In [20, 21], it is found that the dynamic SA architectures perform better than fixed SA architectures, but with higher hardware complexity and power consumption due to the linear increase in the switches with the number of transmit antennas. To

#### *Architectures for Hybrid Precoding and Combining Techniques in Massive MIMO Systems… DOI: http://dx.doi.org/10.5772/intechopen.112113*

reduce the complexity of the dynamic SA, the authors of [22, 23] proposed partially SA structures. However, the partially dynamic precoders in [22, 23] still result in greater computational and hardware complexities, as well as higher power consumption, compared to fixed SA precoders. Recently, deep learning-based hybrid designs have been explored in [24–26]. A new hybrid design approach for SA was studied in [27]. In [27], an iterative algorithm that begins by designing a hybrid precoding and combining matrix for the FA structure and then converts it into a SA matrix by setting certain entries to zero while achieving better performance was proposed and studied.

While the cost and hardware complexity of hybrid precoding and combining for SA architecture are lower than those for the FA architecture, the spectral efficiency achieved through SA architectures is still inferior to that of optimal digital precoding and combining [17]. Therefore, proposing a new hybrid array architecture that balances spectral efficiency, cost, and power consumption is an essential topic. In this chapter, we introduce a new HA architecture for mmWave MIMO systems that aims to achieve a balance between spectral efficiency, cost, and power consumption. Initially, the antennas at the transmitter/receiver are partitioned into subarrays, each containing the same number of antennas as the number of RF chains at the transmitter/receiver, and then divided into nonoverlapping subsets called groups. Finally, the antennas in each group are connected to a group of RF chains in a way similar to the connections in the FA architecture.

The main contributions of this chapter are summarized as follows:

