Multiple-input multiple-output (MIMO) and low-density parity check (LDPC) codes are two of the fundamental technologies in the fifth-generation (5G) networks, where an efficient power allocation scheme is desired to minimize the bit error rate (BER) of the LDPC-coded MIMO system. However, the conventional power allocation methods do not take into account the constraint of modulation and coding scheme (MCS), which may degrade the BER performance. To solve this issue, we propose a deep learning based method to predict the efficient power allocation scheme in coded MIMO systems. Specifically, a neural network is built to learn the complex BER-SNR function to derive the power allocation ratio between the parallel MIMO streams, where the training label is acquired based on the exhaustive searching algorithm. Simulation results show that our proposed method could achieve better BER performance than its conventional counterparts.

Unmanned aerial vehicle (UAV) equipped with large-scale antenna array constitutes a promising relaying candidate for reliable and secure satellite-terrestrial communication. Due to the limitation of energy consumption, a novel UAV architecture with one-bit analog-to-digital converters (ADCs) and one-bit digital-to-analog converters (DACs) is proposed firstly. Leveraging the additive quantization noise model, the exact closed-form expressions of both ergodic capacity and ergodic achievable secrecy rate are derived for UAV-assisted satellite-terrestrial communication systems using large-scale antenna array with one-bit ADCs/DACs. To enhance the transmission capacity and combat the eavesdropper simultaneously, maximum-ratio combining (MRC) is used by UAV to receive signals from satellite and location-based beamforming (LBB) is adopted by UAV to forward signals to destination, where the beamformer is optimized based on the derived expression of the ergodic achievable secrecy rate. Simulation results validate the accuracy of our analytical ergodic achievable secrecy rate, and demonstrate that our proposed MRC/LBB scheme has better secrecy rate than its conventional location-based counterpart.

Soft frequency reuse (SFR) is a widely adopted frequency reuse mechanism to mitigate inter-cell interference (ICI) in cellular networks, which splits resource blocks (RBs) into two groups to serve cell-edge and cell-center users, respectively. In this paper, the joint design of RB grouping, frequency and power allocation is investigated for downlink transmission in the SFR-based multi-cell networks, where the imperfect knowledge of the statistical channel state information (S-CSI) is considered. Firstly, we derive a closed-form ergodic rate and propose a centralized iterative scheme for maximizing the minimum user ergodic rate to achieve user fairness using successive convex approximation (SCA). Furthermore, to facilitate practical implementation, a decentralized scheme is proposed, where the problem is refor-mulated as a global consensus problem and solved by exploiting alternating direction method of multipliers (ADMM). Simulation results demonstrate that our proposed schemes outperform the state-of-the-art counterpart.

The highly directional beams applied in millimeter wave (mmWave) cellular networks make it possible to achieve near interference-free (NIF) transmission under judiciously designed space-time user scheduling, where the power of intra-/ inter-cell interference between any two users is below a predefined threshold. In this paper, we investigate two aspects of the NIF space-time user scheduling in a multi-cell mmWave network with multi-RF-chain base stations. Firstly, given that each user has a requirement on the number of space-time resource elements, we study the NIF user scheduling problem to minimize the unfulfilled user requirements, so that the space-time resources can be utilized most efficiently and meanwhile all strong interferences are avoided. A near-optimal scheduling algorithm is proposed with performance close to the lower bound of unfulfilled requirements. Furthermore, we study the joint NIF user scheduling and power allocation problem to minimize the total transmit power under the constraint of rate requirements. Based on our proposed NIF scheduling, an energy-efficient joint scheduling and power allocation scheme is designed with limited channel state information, which outperforms the existing independent set based schemes, and has near-optimal performance as well.

Beamforming (BF) is usually adopted in millimeter wave wireless systems to compensate high path loss. To increase the received power at user equipment (UE), beam training (BT) is performed to align the beam direction between base station (BS) and UE, where good trade-off between BT overhead and BF gain is preferred. In this letter, two memory-less statistical BT algorithms with finite slots are proposed to reduce the overhead and failure probability of BT, including minimum BT overhead (MO-BT) algorithm and minimum failure probability of BT (MFP-BT) algorithm. Both algorithms make the assumption that memory-less BS randomly select a beam in each slot, based on the prior probability of UE preference, and UE feeds back the beam index to BS to inform which beam should be adopted. By carefully adjusting the probability mass function of beam scanning at BS, MFP-BT can reduce the failure probability of BT, and MO-BT can reduce BT time consumption when compared to their traditional BT counterpart. Numerical results demonstrate the superior performance of our proposed algorithms.

The radio frequency (RF) impairments of analog devices have been regarded as an important factor degrading the performance of Terahertz (THz) communications, where in-phase/quadrature (IQ) imbalance and phase noise (PN) are the two typical RF impairments at THz transceiver. In this paper, we investigate the channel estimation (CE) and equalization for THz receiver in the presence of wideband IQ imbalance and PN, where single-carrier frequency-domain equalization is used. Since PN is inserted between channel impulse response (CIR) and wideband IQ imbalance at the receiver, the CIR and IQ imbalance cannot be treated together as an effective channel. Therefore, a novel two-stage CE method is derived to separately estimate the CIR and wideband IQ imbalance parameters, and its corresponding channel equalization method is designed to compensate both wideband IQ imbalance and PN, and equalizes the CIR. Theoretical and simulation results validate the efficiency of our proposed CE, and the proposed equalization method is shown to outperform the state-of-the-art methods.

As a benefit of using highly directional beams in millimeter wave systems, the downlink inter-cell interference (ICI) imposed on the users can be avoided, provided that the beams of neighbor cells do not point towards the user. We exploit this by designing a protocol for network-coordinated time- domain beam scheduling. Specifically, every pair of two neighbor cells maintains a beam collision table for recording the beam pairs that may inflict ICI upon each other. Then, to avoid beam-collision, the two neighbor cells exchange the necessary information to avoid the simultaneous activation of two beams recorded in one pair. More explicitly, our protocol supports a distributed cell coordination method without requiring any information exchanged between the user and the base station, once the beam collision table has been established. Furthermore, our theoretical analysis and numerical simulations demonstrate that the proposed protocol is capable of efficiently mitigating the ICI between the adjacent cells and hence improves the overall network performance.

The narrow beamwidth characteristic in millimeter wave (mmWave) makes the level of inter-cell interference depend on the beams used by adjacent cells, which indicates that a well-designed schedule of the beams in neighboring cells can reduce the inter-cell interference. In this paper, we investigate a time-domain mmWave beam scheduling problem for interference suppression in a two-cell scenario, where each cell may transmit one or multiple beams simultaneously. Specifically, the beams are one-to-one paired between two adjacent cells, and the simultaneous transmission of two beams in one pair can cause severe inter-cell interference, which is referred to as a pair-wise collision. Instead of optimizing the sum rate of network, we minimize the number of pair-wise collision time slots under the constraint of the service demand of each beam. In this way, the channel state information is not required, and the global optimal solution can be found by our proposed least pair-wise collision (LPC) algorithm, which is a recursive algorithm with linear computational complexity. After that, we extend LPC algorithm to more complicated models, including an asymmetric model and a full-dimension model. Finally, simulations are conducted to verify the efficiency of our methodology.

In this paper, we propose and investigate a low- complexity iterative transmit precoding (TPC) algorithm to enhance the bit error rate (BER) performance for Spatial Modulation (SM) systems over flat-fading multiple-input multiple-output (MIMO) channels. In literature, various TPC techniques have been conceived, but suffer from either over-simplification or lack of generality, thus a low-complexity iterative TPC algorithm is proposed, in which the TPC matrix is diagonal in order to retain the low-complexity benefit of conventional SM systems. Specifically, we investigate a TPC design to maximize the minimum Euclidean distance dmin (max-dmin) between the SM signal points at the receiver side. Different from conventional TPC algorithms, which adjust only two elements in the TPC matrix, all elements of the TPC matrix are iteratively modified until dmin could not be increased. Furthermore, an efficient computing method of dmin is proposed when considering PSK-modulated SM systems. Discussion on complexity analysis and convergence behavior are also provided, which proves the low complexity of the proposed algorithm. Simulation results demonstrate that the proposed TPC algorithm is able to achieve lower BER compared with conventional TPC algorithms.