Low-altitude communications can promote the integration of aerial and terrestrial wireless resources, expand network coverage, and enhance transmission quality, thereby empowering the development of sixth-generation (6G) mobile communications. As an enabler for low-altitude transmission, 3D channel fingerprints (3D-CF), also referred to as the 3D radio map or 3D channel knowledge map, are expected to enhance the understanding of communication environments and assist in the acquisition of channel state information (CSI), thereby avoiding repeated estimations and reducing computational complexity. In this paper, we propose a modularized multimodal framework to construct 3D-CF. Specifically, we first establish the 3D-CF model as a collection of CSI-tuples based on Rician fading channels, with each tuple comprising the low-altitude vehicle’s (LAV) positions and its corresponding statistical CSI. In consideration of the heterogeneous structures of different prior data, we formulate the 3D-CF construction problem as a multimodal regression task, where the target channel information in the CSI-tuple can be estimated directly by its corresponding LAV positions, together with communication measurements and geographic environment maps. Then, a high-efficiency multimodal framework is proposed accordingly, which includes a correlation-based multimodal fusion (Corr-MMF) module, a multimodal representation (MMR) module, and a CSI regression (CSI-R) module. Numerical results show that our proposed framework can efficiently construct 3D-CF and achieve at least 27.5% higher accuracy than the state-of-the-art algorithms under different communication scenarios, demonstrating its competitive performance and excellent generalization ability. We also analyze the computational complexity and illustrate its superiority in terms of the inference time.

In this paper, we propose an efficient simulation method based on adaptive importance sampling, which can automatically find the optimal proposal within the Gaussian family based on previous samples, to evaluate the probability of bit error rate (BER) or word error rate (WER). These two measures, which involve high-dimensional black-box integration and rare-event sampling, can characterize the performance of coded modulation. We further integrate the quasi-Monte Carlo method within our framework to improve the convergence speed. The proposed importance sampling algorithm is demonstrated to have much higher efficiency than the standard Monte Carlo method in the AWGN scenario.

Channel Charting (CC) is a newly developing framework, which aims to learn the low dimensional representation of radio geometry from the channel state information (CSI) collected in the region of interest. But one key question, how soon will channel charting be inapplicable in user moving scenarios, even in certain setting? remains open. In this paper we present some answers by designing corresponding experiments with single cell and multi cell scenarios in LOS channel and multi-path channel settings. Our experimental results suggest that, in the communication scheme with user moving speed = 10 m/s, which is much faster than human walking, the constructed channel charting can provide up to 30 seconds support for future channel estimation. Our work shows that, machine learning and wireless big data based wireless channel learning may support complex communication tasks, especially for massive MIMO system in future communication systems.

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLF) is proposed to reduce the amount of feedback and improve the spectral efficiency of FDD massive MIMO. In most traditional manifold learning approaches, the newly sampled data has to be combined with the existing dataset and the training process has to be done all over again, making it complex to process the incremental CSI in a wireless communication system. Also, the number of component functions required for reconstruction is proportional to the dimension of channel matrix, which limits their practicality in wideband systems. In this paper, we solve the incremental problem by introducing two groups of dictionaries. The key idea of our MLF framework is to learn these dictionaries to represent the manifold structure of CSI data. The incremental CSI is reconstructed by preserving the local manifold structure, i.e., sharing the same neighbor and coding relationships in the input space and the feature space. Experimental results under an industrial channel model of 3GPP show that the proposed algorithm outperforms existing algorithms based on compressive sensing and deep learning in terms of CSI reconstruction performance.

We consider multi-antenna systems carrying OAM waveforms, which are limited by the transmit power and receive antenna aperture. Under the line-of-sight (LOS) channel condition, we propose the multi-mode-multi-spatial (MOMS) solution as a low complex and flexible engineering implementation without any loss of degrees of freedom. The basic idea of MOMS is that the transmitter uses multiple OAM waveforms to carry multiple independent data streams, and the receiver uses a multiple antenna channel equalization algorithm to de-multiplex the multiple eigenchannels. Link-level and Monte Carlo simulation show that our MOMS solution has higher capacity than common uniform linear array LOS-MIMO within the Rayleigh distance of antenna array. However, it has a lower capacity at longer distances due to the faster power intensity decrease of OAM waveforms in comparison with plane waveforms.

True immersion into a remote scenery could not be satisfactory without high definition audiovisual senses and haptic perception of the people and objects in the target environments. We envision the future human-centric applications will evolve with four key trends, namely 1) 360° all-around XR with higher resolution; 2) 3D holographic display integrated with XR; 3) interactive haptic communications; 4) integration of multi-sensory communication. In this paper, we discuss example use cases and typical performance requirements on radio access networks for each of the four trends, hoping to shed a light on the future network design and spectrum utilization.

In this work, we introduce β-expansion, a notion borrowed from number theory, as a theoretical framework to study fast construction of polar codes based on a recursive structure of universal partial order (UPO) and polarization weight (PW) algorithm. We show that polar codes can be recursively constructed from UPO by continuously solving several polynomial equations at each recursive step. From these polynomial equations, we can extract an interval for β, such that ranking the synthetic channels through a closed- form β-expansion preserves the property of nested frozen sets, which is a desired feature for low- complex construction. In an example of AWGN channels, we show that this interval for β converges to a constant close to 1.1892 when the code block-length trends to infinity. Both asymptotic analysis and simulation results validate our theoretical claims.

In this paper we propose a novel robust channel estimation algorithm exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approach is able to cope with a wide range of system and topology scenarios, including those where interference channel may overlap with desired channels in terms of multipath angles of arrival (AoA) or exceed them in terms of received power. We establish the analytical conditions under which the proposed channel estimator is fully decontaminated.

The rapid traffic growth and ubiquitous access requirements make it essential to explore the next generation (5G) wireless communication networks. In the current 5G research area, non-orthogonal multiple access has been proposed as a paradigm shift of physical layer technologies. Among all the existing non-orthogonal technologies, the recently proposed sparse code multiple access (SCMA) scheme is shown to achieve a better link level performance. In this paper, we extend the study by proposing an unified framework to analyze the energy efficiency of SCMA scheme and a low complexity decoding algorithm which is critical for prototyping. We show through simulation and prototype measurement results that SCMA scheme provides extra multiple access capability with reasonable complexity and energy consumption, and hence, can be regarded as an energy efficient approach for 5G wireless communication systems.

In this paper, we consider energy efficient coverage planning in cellular networks. To save energy, each base station (BS) can work in sleep mode when there is no user in its coverage or the users can be served by neighbor base stations. With increasing coverage overlap, there is a tradeoff between the number of BSs per unit area and the proportion of active base stations. Analytical and numerical methods are presented to evaluate coverage planning schemes with different inter-BS distance. Evaluation results show that compared with the minimum coverage overlap scheme, the optimal planning scheme can reduce the network energy consumption by more than 20%, and the performance improvement depends on the user density, network topology, and the power consumption of sleep mode.

To meet the global challenge of reducing greenhouse gas emissions and the explosive demand of wireless data traffic, green architecture design is becoming a critical issue for mobile network operators. Heterogeneous deployments of different cell types have been used to fulfill the challenges mentioned above. In this respect, a critical concern for operators is how to deploy small cells in a green manner such that the global network is spectrum-efficient as well as energy-efficient. In this paper, we characterize the spectrum efficiency (SE) and energy efficiency (EE) for heterogenous wireless networks, taking into account realistic network power consumption model and dynamic network configuration. We give first order closed form analysis to address this issue and show that SE and EE may not be contradictory to each other as in the traditional networks. Our study also provides useful insights for the modeling and deployment of future green wireless networks.

Bandwidth-power tradeoff, as one of the key relations in the green radio research framework, has attracted numerous research attentions in the recent years, due to the refreshed spectrum policies and the popularity of enabling techniques for bandwidth adjustment (such as software defined radio). Previous literatures show some preliminary results on the bandwidth-power tradeoff for heterogeneous networks, however, the practical issues such as site sleeping and inter-cell interference are still out of the research scope. In this paper, we formulate the network energy minimization problem and investigate the optimal bandwidth allocation strategy under the above two issues. Optimal bandwidth-power tradeoff is then derived to show the effects of key system parameters, including cell sizes and interference power levels, followed by some numerical examples to co-verify the analytical results.

In conventional multicast scheme (CMS), the total throughput of multicast group is constrained by the user with the worst channel quality. In order to overcome this problem of limited throughput, the authors introduce a resource allocation algorithm by exploiting layered coding combined with erasure correction coding for multicast services in the downlink of orthogonal frequency‐division multiple access‐based multi‐antenna system. To reduce the feedback overhead of uplink, the authors design a novel transmission scheme with limited feedback. Then, the joint subcarrier and power allocation problem for the data of base layer and enhancement layers are formulated, which is shown to be non‐deterministic polynomial‐ hard. Hence, in order to reduce the computational complexity, they propose a three‐phase suboptimal algorithm. The algorithm is designed to maximise the system throughput, whereas at the same time guarantee the quality of services (QoS) requirements of all multicast groups. It is composed of precoding scheme, proportional fairness subcarrier allocation algorithm and modified water‐filling power allocation algorithm with QoS guarantees (MWF‐Q). To further decrease the complexity of MWF‐Q, a power allocation algorithm with increased fixed power allocation algorithm with QoS guarantees is introduced. Simulation results show that the proposed algorithms based on limited feedback scheme significantly outperform CMS and any other existing algorithm with full feedback. Moreover, the proposed scheme can efficiently reduce 50% of the full feedback overhead.

To meet the global challenge of reducing greenhouse gas emissions and the explosion demand of wireless data traffic, green architecture design is becoming a critical issue for mobile network operators. Heterogeneous deployments of different cell types have been used to fulfill the challenges mentioned above. In this respect, a critical concern for operators is how to deploy small cells in a green manner such that the global network is cost-effective as well as energy-efficient. In this paper, we characterize the energy efficiency (EE) and deployment efficiency (DE) for heterogenous wireless networks, taking into account realistic network power consumption model and dynamic network configuration. We give first order closed form analysis to address this issue and show that a proper density of small cells is required to obtain the maximum achievable EE/DE. Our study also provides useful insights for the modeling and deployment of future green wireless networks.

In conventional multicast scheme (CMS), the total throughput of multicast group is constrained by the user with the worst channel quality. In order to overcome this problem of limited throughput, we consider an opportunistic multicast scheduling (OMS) based on layered coding. A novel subcarrier and bit allocation algorithm is exploited for targeting the maximum throughput (MT) of a whole multicast group while at the same time guaranteeing the quality of services (QoS) requirements of all users. In this paper, we propose an optimal resource allocation algorithm in the downlink of OFDMA-based wireless multicast group. A two-phase suboptimal algorithm is proposed to reduce the computational complexity. Simulation results show that the performance gap between the optimal algorithm and the proposed suboptimal algorithm is quite small. The proposed algorithm significantly outperforms CMS. Moreover, it obtains more throughput than another existing algorithm.

This paper propose an optimal resource allocation algorithm by exploiting opportunistic multicast scheduling (OMS) scheme using layered coding combined with erasure correction coding for multicast services in the downlink of OFDMA-based single frequency networks (SFN). In this algorithm, we exploit unequal error protection (UEP) Reed-Solomon (RS) coding to compensate for possible data packet loss of base layer and enhancement layers which are obtained from fine-granularity-scalability (FGS) video coding. In order to reduce the feedback load, we only use the knowledge of average channel state information (CSI) for the users. The proposed algorithm maximizes the enhancement layers throughput while guaranteeing the minimum data rate of the base layer in each multicast group. A two-phase suboptimal algorithm is proposed to reduce the computational complexity. Simulation results show that the performance of the algorithm with UEP RS coding is more than the algorithm without UEP RS coding. Moreover, the proposed algorithms significantly outperform conventional multicast scheme (CMS).

In conventional multicast scheme (CMS), the total throughput of multicast group is constrained by the user with the worst channel quality. In order to overcome this problem of limited throughput, we consider a resource allocation algorithm based on layered coding, when a limited feedback scheme is considered. A novel subcarrier and power algorithm is exploited for targeting the maximum throughput (MT) of enhancement layers while at the same time guaranteeing the quality of services (QoS) requirements of all users. In this paper, a three-step subcarrier allocation algorithm and two power allocation algorithms are proposed. Simulation results show that the proposed algorithm significantly outperforms CMS. Moreover, it obtains more throughput than another existing algorithm.

This paper addresses the power control problem in wireless networks where transmitters choose their control policy freely and selfishly in order to maximize their individual energy-efficiency. In this framework, two new scenarios are studied in details: 1. a scenario where a fraction of the transmitters can observe the power levels of the other transmitters while the latter have no sensing capabilities; 2. a scenario where the observation structure is triangular, that is, the kthtransmitter can observe the k - 1thtransmitters (which corresponds to a multi-level hierarchical game). In both scenarios the equilibrium analysis (existence, uniqueness, determination, efficiency) is conducted. In scenario 1, it is proved that the game outcome Pareto dominates the one obtained when no transmitters can sense the others. Taking the sensing cost into account, a simple condition under which being a follower (namely a transmitter who senses) is better than a leader is provided. Interestingly, the existence of an optimum fraction of cognitive transmitters in terms of sum utility is proved in the case where the sensing cost is neglected. In scenario 2, it is proved analytically that knowing more leads to a better utility and the game outcome Pareto dominates the solution with no sensing. The derived results are illustrated by numerical results and provide some insights on how to deploy cognitive radios in heterogeneous networks in terms of sensing capabilities.

This paper describes a multiuser power allocation strategy for fixed constellation over parallel Gaussian channels (e.g. OFDM systems). The criterion under consideration is mutual information, given arbitrary input distributions over users and over subcarriers. The algorithm achieves with very low complexity the multi-user aggregate sum mutual information upper bound. The algorithm is based on an iterative Mercury/waterfilling procedure. Moreover, we extend the framework to a decentralized scenario using a linear approximation of the MMSE function. We show, in particular that each user can, under certain assumptions, independently determine the power allocation without knowing the channel information of other users. Simulation results validate the theoretical claims.