As a critical component of sixth-generation (6G) wireless networks, ultra-reliable and low-latency communication (URLLC) is expected to support real-time and reliable information exchange in low-altitude environments. However, achieving URLLC often incurs significant resource overhead, including increased bandwidth consumption, higher transmit power, and denser access point (AP) deployment, which pose significant challenges to both spectral efficiency (SE) and energy efficiency (EE). Besides, existing iterative optimization algorithms are computationally intensive and struggle to meet the latency requirements of URLLC. To address these challenges, we propose a hybrid aerial-terrestrial cell-free massive MIMO (CF-mMIMO) network to support diverse services, along with a channel prediction network and a deep mixture of experts (MoE) network for uplink optimization. First, we design a channel prediction network (CP-Net) to mitigate channel aging caused by high-mobility user equipment (UE). CP-Net employs three Transformer-based sub-networks for aged channel state information (CSI) prediction, while a channel quality-aware loss function is introduced to improve the prediction accuracy of weak links. Based on the predicted CSI, we develop a deep MoE network (MoE-Net) for power allocation comprising three expert models targeting different objectives. Then, we introduce a weighted gating network (WT-Net) to learn an efficient adaptive combination of expert outputs. The proposed framework better captures heterogeneous UE requirements and improves communication performance under URLLC constraints. Numerical results demonstrate the effectiveness of the proposed method.

With the rapid advancement of unmanned aerial vehicle (UAV) technology, UAV cell free massive multiple-input multiple-output (CF-mMIMO) systems demonstrate significant potential for enhancing wireless communication performance. However, optimizing energy efficiency (EE) to meet the growing demands of communication has become a critical research challenge. This paper explores resource allocation in the UAV CF-mMIMO uplink under ultra reliable and low latency communication (URLLC) constraints to improve EE. We first conduct a systematic analysis in the system model to identify key factors impacting EE. Accounting for these factors, an optimization problem is then formulated. To solve this problem, we employ a self-supervised deep neural network (SSDNN) composed of two submodules: One is to extract channel characteristics of all users equipment (UEs), and the other is to optimally allocate power for both UAVs and ground user equipments (GUEs) via a resource allocation layer. Numerical results show that our proposed method achieves higher EE compared to approaches that do not consider GUE interference.

To achieve ultrareliable and low-latency communication (URLLC) and support high density of wireless connections simultaneously, the sixth-generation Industrial Internet of Things (6G-IIoT) necessitates an expansion of antenna arrays and broader bandwidths, which suffers from high energy consumption. To address this issue, this article investigates a cell-free massive multiple-input-multiple-output (CF-mMIMO) system and designs an iterative search-based two-stage energy efficiency (EE) optimization algorithm for the uplink communication of the system. The first stage prioritizes reliability to ensure that all users meet the URLLC requirements regarding latency and reliability. The second stage maximizes the EE under URLLC-satisfied conditions. Considering that iterative search algorithms incur a high computational overhead and variable number of iterations, we further propose a convolutional neural network architecture (RACNN) to approximate an optimal resource allocation strategy and to realize the real-time and stable output. This structure extracts deep correlations among users from the global channel features. It takes strategies of multitask learning and weight loss adaptation to improve the model’s convergence speed. Furthermore, we employ deep transfer learning to adjust RACNN parameters to accommodate the potential of dynamic communication scenarios, thereby reducing the demand for training samples and training time overhead. Finally, the efficacy of the proposed algorithm, RACNN, and deep transfer learning is validated through experimental simulations.

For ultra-massive multiple-input multiple-output (MIMO) systems, minimum mean square error (MMSE)-based detection suffers from prohibitive complexity due to the large-dimension matrix operations. Among various low-complexity variants of the MMSE, the implicit detection is promising as it omits the explicit computation of Gram matrix and its associated inversion. However, the performance degradation is non-negligible when the system loading factor increases and the correlated channel is considered. To address this issue, this letter proposes an enhanced implicit detector which combines Cauchy-Barzilai-Borwein (CBB) iteration and Neumann series (NS) iteration by means of a dynamic weighted factor, named W-CBBNS. A simple approach to determine the weighted factor by maximizing the estimated symbol vector reliability is developed. Besides, the complexity is remarkably reduced by exploiting scalar-vector and matrix-vector multiplication compared to the CBB-based explicit detection. Numerical results show that the W-CBBNS significantly outperforms the existing implicit detectors such as the weighted NS and steepest descent-non-stationary Richardson, while at a similar magnitude of complexity, and also outperforms the state-of-the-art CBB-based detector.

In space-air-ground integrated networks (SAGIN), receivers experience diverse interference from both the satellite and terrestrial transmitters. The heterogeneous structure of SAGIN poses challenges for traditional interference management (IM) schemes to effectively mitigate interference. To address this, a novel UAV-RIS-aided IM scheme is proposed for SAGIN, where different types of channel state information (CSI) including no CSI, instantaneous CSI, and delayed CSI, are considered. According to the types of CSI, interference alignment, beamforming, and space-time precoding are designed at the satellite and terrestrial transmitter side, and meanwhile, the UAV-RIS is introduced for the cooperating interference elimination process. Additionally, the degrees of freedom (DoF) obtained by the proposed IM scheme are discussed in depth when the number of antennas on the satellite side is insufficient. Simulation results show that the proposed IM scheme improves the system capacity in different CSI scenarios, and the performance is better than the existing IM benchmarks without UAV-RIS, but the performance improvement is at the cost of the requirement on the elements of UAV-RIS.

In MIMO multi-user networks, inter-user interference (IUI) significantly affects the system performance. To handle this problem, this paper proposes the reconfigurable intelligent surface assisted cooperative interference alignment scheme (RIS-CIA). The core idea of this work is that the base station and full-duplex users jointly design space-time precoding matrices, which can reduce the dimension of the interference space on the user side. Besides, the additional interference caused by the information exchange process is split into sub-blocks by space-time precoding, then eliminated by interference nulling assisting by the passive RIS. The simulation results show that the RIS-CIA scheme with few numbers of elements obtains higher DoF than that of benchmark schemes with a huge number of elements.

In heterogeneous networks (HetNets), although deploying numerous small base stations (SBSs) can effectively enhance spectral efficiency (SE), it is difficult to achieve seamless coverage due to their fixed locations. To handle this issue, we propose a HetNet structure assisted by unmanned aerial vehicles (UAVs), where the high mobility and flexible deployment of UAVs are leveraged. However, interference is inevitable in the proposed UAV-assisted HetNet, thus we design a comprehensive interference management (IM) scheme, selectively adopting successive interference cancellation (SIC) algorithm, space-time interference alignment (STIA) and interference steering (IS) according to the location of users and interference types. The numerical results verify that with SIC-STIA-IS scheme, the proposed UAV-assisted HetNet is advantageous in degrees of freedom (DoF) and sum rate.

To accommodate the explosive growth of the Internet of Things (IoT), incorporating interference alignment (IA) into existing multiple access (MA) schemes is under investigation. However, when it is applied in MIMO networks to improve the system capacity, the new problem regarding information delay arises which does not meet the requirement of low-latency. Therefore, in this paper, we first propose a new metric named degree of delay (DoD) to quantify the issue of information delay. By analyzing DoD with classical transmission schemes, it can be seen that the information latency does affect the performance of the system. To cope with this issue, hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR) is first proposed. It achieves optimal performance in 2-user MIMO scenarios, but suffers a performance loss in$K$-user MIMO scenarios. Then, the improved HAA-PIE-RIR scheme (HAA-IPIE-RIR), and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR) are proposed. The former achieves optimal performance in$K$-user MIMO scenarios, but requires heavy computational cost. The latter is a trade-off scheme considering performance and computational cost comprehensively. Overall, our proposed schemes can obtain lower DoD and higher DoF than that of traditional IA schemes.

In fifth generation networks (5G), beamforming technique is widely used to obtain higher system capacity, but it cannot eliminate inter-user interference (IUI) of networks due to excessive number of users. To handle this problem, interference alignment (IA) schemes attract great attention as they can effectively restrain IUI. However, the existing IA schemes cannot achieve antenna adaptation and the obtained degree of freedom (DoF) may be not optimal. In this paper, a novel antenna adaptation based interference elimination and regeneration (AA-IER) scheme is proposed for cooperative networks, where a relay with hybrid antenna array structure is adopted to assist the communication. The proposed transmission process is completed in two phases, including interference elimination phase (IEP) and interference regeneration phase (IRP). For the former, the IUI is eliminated and the redundant symbols are erased so that the received signal of multiple users can be decoded simultaneously. For the latter, the redundant symbols of all users are regenerated where the space resources are fully utilized. The simulation results show that AA-IER scheme obtains higher DoF than that of three benchmark schemes. Meanwhile, it requires fewer antennas of relay than HAA-CIE-RIA scheme.

As a novel method to increase channel capacity of networks, interference alignment (IA) technique has attracted wide attention in fifth-generation wireless networks (5G). However, when it is applied in interference networks, the problem regarding information delay arises which has not been well addressed yet. In this paper, we formally propose the novel concepts of degree of delay (DoD) to quantify the issue of information delay and analyze its determining factors, i.e., delay sensitive factor, queueing delay slot and size of dataset. To reduce DoD, three novel joint IA schemes are proposed for broadcast channel (BC) networks with different amounts of users, i.e., hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR), HAA based improved PIE and RIR scheme (HAA-IPIE-RIR) and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR). Among the three, the second scheme extends the application scenarios of the first scheme from 2-user to K-user while bringing huge computational complexity burden. The third scheme relieves the burden but it leads to slight degree of freedom (DoF) loss. Overall, the proposed schemes achieve higher DoF and lower DoD than the existing IA schemes.

Abstract This paper discusses an artificial noise‐aided intelligent reflecting surface‐MIMO–OFDM system physical layer secure communication, in which two cases for the intelligent reflecting surface reflection coefficient models are considered separately, that is unit modulus constraint for the reflection coefficients and the more practical situation of amplitude phase‐shift dependence. Then the problem of joint optimisation for the precoding matrix, artificial noise covariance matrix and intelligent reflecting surface reflection coefficient matrix to maximise the sum secrecy rate under the power constraint at the transmitter is formulated, and then an alternate optimisation‐based inexact block coordinate descent algorithm is proposed to tackle the formulated non‐convexity problem. For the problem with unit modulus constraint for the intelligent reflecting surface reflection coefficients, closed‐form solutions of the optimisation variables are obtained by utilising the Lagrange multiplier method and the complex circular manifold method. For the problem with intelligent reflecting surface reflection coefficient amplitude phase‐shift dependence, alternate optimisation‐based penalty method is used to obtain the intelligent reflecting surface optimal reflection matrix. Numerical results indicate that the algorithm for the intelligent reflecting surface reflection coefficient unit modulus constraint achieves the maximum secrecy rate, and the algorithm for the intelligent reflecting surface reflection coefficient of amplitude phase‐shift dependence has the sub‐optimal performance, and the benchmark schemes such as no intelligent reflecting surface and intelligent reflecting surface random phase shift strategies have the worst and similar performance.

In this paper, a new class of soft-input soft-output block decision feedback equalizer (BDFE) for turbo equalization over inter-symbol interference (ISI) channels under minimum mean-square error (MMSE) criterion is proposed. First, the BDFE which employs both the a posteriori moments of estimated symbols and the a priori moments of unestimated symbols is investigated, and two recursive schemes are derived to reduce the complexity. Then, to mitigate error propagation, the bidirectional BDFE (Bi-BDFE) consisting of a forward BDFE and a backward BDFE is proposed. Furthermore, a threshold is employed to reduce the number of inverse matrix update process for the BDFE (T-BDFE), which can attain a flexible trade-off between complexity and performance. Finally, the bidirectional equalization is introduced into a low-complexity approximate solution of the BDFE (Bi-LC-BDFE) to further reduce the complexity and obtain time diversity gain, which utilizes a time-invariant covariance matrix. Simulation results show that the proposed equalizers significantly outperform the block linear equalizer (BLE), the exact MMSE-based linear equalizer (Exact-MMSE-LE) and the soft-decision feedback equalizers (SDFE), while keeping a similar complexity as the BLE. In addition, the Bi-BDFE can achieve better performance than the Max-Log-MAP equalizer while its complexity is on the same order as the BDFE.

This paper investigates the cooperative full-duplex device-to-device (D2D) communication underlaying a cellular network, where the cellular user (CU) acts as a full-duplex relay to assist the D2D communication. To simultaneously support D2D relaying and uplink transmission, superposition coding and successive interference cancellation are adopted at the CU and the D2D receiver, respectively. The achievable rate region and joint outage probability are derived to characterize the performance of the considered system. In consideration of the fairness between the cellular uplink and the D2D link, an optimal power allocation scheme is proposed to maximize the minimum achievable rate of them. Besides, by analyzing the upper bound of the joint outage probability, we study a suboptimal power allocation to improve the outage performance. The simulation results confirm the theoretical analysis and the advantages of the proposed power allocation schemes.

This paper proposes novel transmission schemes for a class of interference networks that can achieve new tradeoff regions between the sum of degrees of freedom (sum-DoF) and channel state information (CSI) feedback delay with distributed and temperately-delayed CSI at the transmitter (CSIT). A significant impact of the results is they reveal that distributed and temperately-delayed CSIT contributes to achieve better sum-DoF than that without CSIT in a certain class of interference networks. Specifically, a distributed space-time interference alignment (STIA) scheme is proposed for the two-user multiple-input multiple-output (MIMO) X channel via a novel precoding method called Cyclic Zero-padding. The achieved sum-DoFs herein for certain antenna configurations are greater than the best known sum-DoFs in literature with delayed CSIT. Furthermore, we propose a distributed retrospective interference alignment (RIA) scheme that achieves more than 1 sum-DoF for the K-user single-input single-output (SISO) X network. Finally, we extend the distributed STIA to the M×N user multiple-input single-output (MISO) X network, where each transmitter has N - 1 antennas and each receiver has a single antenna, yielding the same sum-DoF as that in the global and instantaneous CSIT case. The discussion and result of the MISO X network can be extended to the MIMO case due to the spatial scale invariance property.