This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. Furthermore, a conventional LMA codebook with codewords uniformly distributed on a hypersphere may not be channel-adaptive and may lead to increased signal detection complexity. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly. The proposed FAS-based system addresses the SAS structural problems and can support larger numbers of users. For LMA-imposed constant-power downlink precoding, we propose an FAS-based normalized block diagonalization (FAS-NBD) algorithm. However, the forced normalization may result in performance degradation. This degradation, together with the aforementioned codebook design problems, is difficult to solve analytically. This motivates us to propose a Deep Learning-enhanced (FAS-DL-NBD) algorithm for adaptive codebook design and codebook-independent decoding. It is shown that the proposed algorithms are robust to imperfect knowledge of channel state information and yield excellent error performance. Moreover, the FAS-DL-NBD algorithm enables signal detection with low complexity as the number of bits per codeword increases.

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth-generation (5G) mobile networks and beyond. In this paper, we consider uplink communications with a hybrid beamforming structure and focus on improving the spectral efficiency (SE) and energy efficiency (EE) of mmWave multiple-input multiple-output (MIMO)-NOMA systems with enhanced user grouping and power allocation. It is noted that the optimization of the SE/EE is a challenging task due to the non-linear programming nature of the corresponding problem involving user grouping, beam selection, and power allocation. Our idea is to decompose the overall optimization problem into a mixed integer problem comprised of user grouping and beam selection only, followed by a continuous problem involving power allocation and digital beamforming design. Exploiting the directionality property of mmWave channels, we first propose a novel initial agglomerative nesting (AGNES) based user grouping algorithm by taking advantage of the channel correlations. To avoid the prohibitively high complexity of the brute-force search approach and to address the overlapping beam problem, we propose two suboptimal low-complexity user grouping and beam selection schemes, the two-stage direct AGNES (D-AGNES) scheme and the joint successive AGNES (S-AGNES) scheme. We also introduce the quadratic transform (QT) to recast the non-convex power allocation optimization problem into a convex one subject to a minimum required data rate of each user. The continuous problem is solved by iteratively optimizing the power and the digital beamforming. Extensive simulation results have shown that our proposed mmWave-NOMA design outperforms the conventional orthogonal multiple access (OMA) scenario and the state-of-art NOMA schemes.

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) transmission are two key enabling technologies for the fifth-generation (5G) mobile networks and beyond. In this paper, we consider mmWave NOMA systems with max-min fairness constraints. On the one hand, existing beamforming designs aiming at maximizing the spectral efficiency are unsuitable for the NOMA systems with fairness considered in this paper. On the other hand, previous work on mmWave NOMA mostly depends on full knowledge of channel state information (CSI) which is extremely difficult to obtain accurately in mmWave communication systems. To address this problem, we propose a heuristic hybrid beamforming design based on a statistical CSI (SCSI) user grouping strategy. An analog beamforming scheme is first proposed to mitigate inter-cluster interference in a first stage. Then two digital beamforming designs are proposed to further suppress the interference based on SCSI. One is the widely used zero forcing approach and the other is derived from the signal-to-leakage-plus-noise ratio metric extended from orthogonal multiple access systems. The effective gains fed back from the users are used for power allocation. We introduce the quadratic transform method and bisection approach to reformulate this complex problem so as to render it solvable. Simulation results show that our proposed algorithms outperform existing algorithms in term of user fairness.

Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the load-modulated multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CPA). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum likelihood (ML) detection scheme incurs high complexity which increases exponentially with the number of transmitting antennas and the number of bits carried per antenna in the PMH modulation. To detect the PMH signals without knowing the prior CSI, we first propose a signal detection scheme, termed as the hypersphere clustering scheme based on the expectation maximization (EM) algorithm with maximum likelihood detection (HEM-ML). By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector. To further reduce the computational complexity in the ML detection in HEM-ML, we also propose the second signal detection scheme, termed as the hypersphere clustering scheme based on the EM algorithm with KD-tree detection (HEM-KD). The CSI obtained from the EM algorithm is used to build a spatial KD-tree receiver codebook and the signal detection problem can be transformed into a nearest neighbor search (NNS) problem. The detection complexity of HEM-KD is significantly reduced without any detection performance loss as compared to HEM-ML. Extensive simulation results verify the effectiveness of our proposed detection schemes.

In this paper, we propose two novel user grouping strategies for the non-orthogonal multiple access (NOMA) technique based hybrid millimeter wave (mmWave) systems. In NOMA technology, user grouping is the first step to reduce the inter-group interference and will impact the design of hybrid beamforming. In contrast to some previous user grouping strategies that allocate equal or close number of users in each group, our proposed user grouping algorithms allow different number of users per group, which enables the users to get better beam gain. We propose two user grouping algorithms, the strong user dependent (SUD) user grouping algorithm and the Agglomerative Nesting (AGNES) clustering user grouping algorithm, in which a user is grouped to the beam with the largest beam gain. In the first algorithm, the transmitter selects the strong users as the beam centroids and allocates the rest weak users to the corresponding beam groups with the largest beam gain. The second algorithm directly forms groups based on the AGNES clustering algorithm without the predefined beam centroids. Simulation results demonstrate the effectiveness of our proposed user grouping strategies.

Hybrid beamforming architecture is a practical implementation in millimeter-wave (mmWave) communication systems with large-scale antenna arrays for future fifth-generation (5G) cellular networks, which offers a compromise between hardware complexity and system performance. Fully-connected and sub-connected are two popular connected structures in the hybrid beamforming architecture. However, the hardware complexity and the required number of phase shifters in the fully-connected structure is rather high when the number of RF chains increases. Sub-connected structure can effectively decrease the required number of phase shifters but at the expense of beamforming gain loss of each RF chain. In this paper, we consider an overlapped subarray structure between fully-connected and sub-connected structures and determine how many antenna elements should be connected for each RF chain. For the mmWave downlink multi-user multiple-input multiple-out (MIMO) communication, we design a two-stage hybrid beamforming algorithm based on the overlapped subarray structure. Simulation results indicate that there exists a best cost-effective overlapped subarray spacing associated with the required number of phase shifters for the sparse mmWave channel, which provide a guideline for the design of hybrid beamforming architecture.

In this paper, we propose a new Laurent decomposition based low complexity detection scheme for M-ary continuous phase modulation (CPM) signals. By introducing per survivor processing (PSP) algorithm in the demodulation of Laurent-based CPM signals, the proposed scheme simplifies the trellis structure, which reduces the detection complexity of CPM signals. Numerical results reveal that the proposed algorithm can significantly reduce the detection complexity at the price of slight bit-error-rate (BER) disgrace as compared with the conventional detection schemes.