Optimizing power allocation among access points (APs) is an effective way to improve the spectral efficiency of user-centric cell-free multi-antenna systems, where each user equipment (UE) associates with adjacent APs, leading to dynamic association relationship. The serving APs can adopt different beamforming strategies, say distributed beamforming (D-BF) and centralized beamforming (C-BF). Although D-BF requires lower fronthaul capacity than C-BF, it is unable to eliminate inter-AP interference. To reduce the high computational complexity of numerical algorithms for solving optimization problems, deep neural networks (DNNs) have been designed to learn the power allocation policies with D-BF, which however disregard the impact of dynamic association and are with high training complexity. Besides, there are no existing works to learn the power allocation policies with C-BF, where the power constraint of each AP needs to be satisfied. In this paper, we design graph neural networks (GNNs), including D-GNN and C-GNN, to learn the power allocation policies under D-BF and C-BF, respectively. For achieving high learning efficiency, these GNNs exploit the permutation properties of the optimal policies. The D-GNN is adaptive to dynamic association and the C-GNN can satisfy the per-AP power constraint. Simulation results show that the designed GNNs outperform the existing DNNs with low training complexity and short inference time, and are generalizable to the numbers of APs, antennas, and UEs.

Graph neural networks (GNNs) have been used for optimizing resource allocation due to their potential in scalability and size generalizability. To facilitate their application to large- scale wireless systems, decentralized inference with GNNs has been investigated recently. Yet decentralized training of GNNs at wireless nodes, which can alleviate the computing load at central server and protect privacy of users, has never been studied. In this paper, we strive to train GNNs in mobile systems in a decentralized manner, by taking power control optimization for interference coordination as an example. We present a framework for decentralized training of GNNs at wireless nodes, and propose two algorithms to tackle the challenge of training GNNs over dynamic graph topology. Simulation results show that the power control policy learned by the GNN performs very close to decentralized numerical algorithms with lower signaling overhead for inference.

Characterization of morphological organization pattern in individual brain has long been an open question. Recently, a novel single-subject network that built upon multiple morphological features (MFN) was introduced [1], which exhibited extraordinary power in diagnosing patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). This study aims to characterize the cortico-cortical topological organization in MCI and AD cohorts via the MFN, and uncover the structural substrate that leads to the high classification accuracy. The MFNs were constructed for 165 normal controls (NCs), 221 patients with MCI, and 142 patients with AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Results from graph theoretical analysis showed `small-world' property and modular organization of the MFN in three groups; as well as increased local clustering and characteristic path length, and altered hub regions in patients with AD and MCI. More importantly, we found the primary atrophic regions were accompanied by increased number of in-flow connections; whereas, the connections that linked to the less atrophic regions were mostly out-flow connections. This phenomenon, we speculated, might indirectly reflect the trophic supporting effects in the brain. Our results demonstrated the basic organizational principles of the MFN and its changes in patients with AD and MCI, providing important implications of what makes the MFN powerful in auto-diagnosis of mental disorders.

In this paper, we strive to improve the throughput of heterogeneous cellular networks by exploiting the pre-cached files at user end to manage interference. We consider a transmission scheme, where network-coded multicast is employed to help cancel multi-user interference, and reconstructed interference cancellation (RIC) is used to help eliminate inter-cell interference. Because RIC is opportunistic, base station (BS) muting is used to coordinate the residual strong interference. Since user association affects residual interference and is coupled with BS muting while both are operated in a very different timescale from content caching, we jointly optimize user association and BS muting for a given caching policy to maximize the number of users simultaneously served by the transmission scheme. By transforming the formulated problem into a maximal independent set problem with constructed conflict graph, the global optimal solution is found with graph theory methods. By exploiting the topology feature of heterogeneous networks, we proceed to propose two low-complexity algorithms, respectively, implemented in a centralized and distributed manner, which are viable for large-scale networks. Simulation results show that the optimized transmission scheme achieves a remarkable performance gain over the existing schemes.

In this paper, we strive to improve the throughput of wireless networks by exploiting the pre-cached files at user end to manage interference. We employ network-coded multicast (NCM) to help cancel multi-user interference, and reconstructed interference cancellation (RIC) to help cancel inter-cell interference. Because inter-cell interference may not be thoroughly canceled by RIC, base station (BS) muting is used to coordinate the residual strong interference. We jointly optimize user association and BS muting to maximize the number of users simultaneously served by a system using NCM and RIC with any given caching policy. Owing to the complex relation among BS muting, user association, NCM, and RIC, the formulated problem is NP-hard. By transforming the problem into the well-known maximal independent set problem with constructed conflict graph, the global optimal solution can be found efficiently with graph theory methods. Simulation results show that the proposed transmission strategy achieves a remarkable performance gain.

Proactive caching in base station (BS) is a promising way to leverage the human-behavior related information to boost the system throughput and improve user experience with low cost. Yet existing caching policies are "blind" to users, i.e., the users are unaware about whether the files to be requested are locally cached at the BS around them, whose performance will degrade in mobile systems due to the randomness of user behavior. For example, when a user stays in a cell where the BS caches the file to be requested later, the user may not initiate the request. When the user sends the request, it may have entered another cell where the BS does not cache the requested file. In this paper, we introduce a simple idea of informing the users about what the BS has cached to make local caching efficient, which can be regarded as a form of recommendation. Since the probabilities that the users request the files are unknown, we resort to Q-learning to perceive the request probability and the statistics of random arrival and departure of mobile users. Then, a cache replacement policy is optimized. Simulation results demonstrate that the proposed policy with recommendation can provide higher long-term system reward than existing policies without recommendation.

Energy efficiency (EE) has become an important design goal for mobile communication systems. Since the EE of a system is evaluated during a period of time, a transmission strategy designed from maximizing instantaneous EE (INEE) may not achieve the maximal EE of the system. To exploit the accumulative nature of the energy, accumulative EE (ACEE) can be used as the objective function, which is the ratio of the accumulated amount of data transmitted to the accumulated energy consumed until the time for optimization. In this paper, we study when ACEE is beneficial to EE-oriented optimization. By taking a single user multi-antenna multi-subcarrier system serving two classes of traffic as an example, we formulate three problems to optimize rate allocation among subcarriers and time slots respectively maximizing the INEE, ACEE and EE upper-bound, which can be easily extended to multi-user systems. We proceed to analyze the behavior and performance of the corresponding solutions. Analytical and simulation results show that using ACEE yields a more energy efficient design for the systems serving best effort traffic with less transmit antennas, serving less users simultaneously or at low signal to noise ratio under time-varying channels. However, the conclusions for real-time traffic are different.

In this paper, we study the degrees of freedom (DoF) of partially-connected symmetrically-configured multi-input-multi-output interference broadcast channel (MIMO-IBC) and the impact of partial connectivity on the DoF. We investigate the information theoretic maximal DoF and maximal DoF achieved by linear interference alignment (IA) for the symmetrically-connected symmetrically-configured MIMO-IBC, and prove that the DoF are achievable by asymptotic IA and linear IA for the asymmetrically-connected symmetrically-configured network when the maximal number of interfering cells seen at each BS and each user (denoted as the degrees of connectivity) is bounded. We find that for the symmetrically-connected symmetrically-configured MIMO-IBC, the maximal achievable DoF are independent of the number of total cells but depend on the degrees of connectivity. When the degrees of connectivity decrease, the maximal DoF achieved by linear IA are close to the information theoretic maximal DoF.

In this paper, we study the degrees of freedom (DoF) of the asymmetric multi-input-multi-output interference broadcast channel (MIMO-IBC). By introducing a notion of connection pattern chain, we generalize the genie chain proposed in [10] to derive and prove the necessary condition of IA feasibility for the asymmetric MIMO-IBC, which is denoted as irreducible condition. It is necessary for both linear interference alignment (IA) and asymptotic IA feasibility in MIMO-IBC with arbitrary configurations. In a special class of the asymmetric two-cell MIMO-IBC, the irreducible condition is proved to be the sufficient and necessary condition for asymptotic IA feasibility, while the combination of proper condition and irreducible condition is proved to the sufficient and necessary condition for linear IA feasibility. From these conditions, we derive the information theoretic maximal DoF and the maximal DoF achieved by linear IA. The derived maximal DoF per user for the asymmetric two-cell MIMO-IBC are also the upper-bounds of DoF per user for the asymmetric G-cell MIMO-IBC.

Energy efficiency (EE) has become an important design goal for wireless systems. Since the EE of a system is evaluated in a duration where the channel may vary, designing a transmission strategy to maximize instantaneous EE may lead to a loss in achievable EE of the system. To exploit the accumulative information of throughput and energy, accumulative EE (ACEE) can be used as the objective function, which is the ratio of the accumulated throughput divided by the overall energy consumed over the past time slots. By analyzing the solutions of three EE maximal problems to optimize rate allocation among multiple subcarriers in multiple time slots, we show when and why the ACEE can achieve high system EE. Simulation results verify our theoretical analysis. Our analysis show that the ACEE is beneficial for the systems with low circuit power consumption under time-varying channels when either the data rate requirement or signal to noise ratio is low, and either the number of antennas or subcarriers is small.

In this paper, we study the maximal achievable degrees of freedom (DoF) for G-cell, K-user M×N symmetric multi-input-multi-output (MIMO) interference broadcast channel (MIMO-IBC) with constant coefficients by linear interference alignment (IA). We start by analyzing the sufficient and necessary conditions of the IA feasibility, which are associated with generalized Fibonacci sequences. Except for the well-known proper condition, we find another condition necessary to ensure a kind of irreducible interference to be eliminated, denoted as irreducible condition. We proceed to find the minimal number of antennas at each base station and each user to support a required number of interference-free data streams, from which we obtain the maximal achievable DoF. We find that the feasibility conditions in terms of the required antenna resources can be divided into two regions according the ratio of ρ = M/N. If ρ falls into the region that characterized by the proper condition, the achievable DoF per user with spatial extension is (M + N)/(GK + 1). If ρ lies in the region that characterized by the irreducible condition, the DoF is a piecewise linear function of M or N alternately.

This paper studies the degrees of freedom (DoF) of relay-aided interference broadcast channels and the relay resource to achieve them. We show that, with the aid of half-duplex relays, a G-cell system can achieve GMBS/2 DoF, where MBSis the number of transmit antennas in each cell. By studying the interference-free constraints, we obtain a lower bound of the relay resource that ensures interference-free transmission. Instead of directly solving the complicated multivariate problem with cubic interference-free constraints, we propose to relax the problem to linear equations by randomly initiating certain variables, and derive an achievable bound on relay resource. Numerical results show that the relay-aided interference broadcast channels require less relay resource than relay-aided interference channels and transmission protocol has large impact on the required resources.

In this paper, we develop linear interference alignment (IA) approach for multi-input-multi-output interference broadcast channel (MIMO-IBC). Since multiple data streams from each base station (BS) to multiple mobile stations (MSs) experience identical channel, it is hard to ensure the rank constraint to the intended MSs in the desired cell meanwhile ensure the interference aligned at the unintended MSs in other cells. Considering the difficulty in aligning interference at the receiver in MIMO-IBC, we design a transceiver to align and eliminate the interference at the transmitter. Specifically, we first design receive vectors of all MSs to align the inter-cell interference at the BS side, and then design the precoder of each BS to eliminate all the intra- and inter-cell interference. The proposed approach can be applied for general MIMO-IBC and has closed-from solutions for some antenna configurations. Simulation results validate our analysis and show that the proposed IA transceiver can achieve the maximal degrees of freedom of MIMO-IBC and attain a good trade-off between the maximal number of data streams and signal-to-noise ration gain.

Two-way relay (TWR) transmission provides high spectral efficiency when one-pair of two users exchange information via a single relay. However, in multiuser relay systems where multi-pair of users exchange messages, if the relay does not have sufficient degrees of freedom, the gain of TWR communication may vanish. Fortunately, signal alignment (SA) signaling can recover the spectral efficiency of TWR transmission in multiuser scenarios, which is originally proposed for multiple-input-multiple-output (MIMO) systems. In this paper we investigate signal alignment for multicarrier code division multiple access (MC-CDMA) TWR systems. Due to the difference in channel characteristics and degrees of freedom, the existing SA signalings designed for MIMO TWR systems do not always perform well in MC-CDMA TWR systems. By exploiting the unique features of both TWR systems and MC-CDMA channels, we propose a spectral-efficient SA signaling for MC-CDMA TWR systems, where each pair of users employ a maximal ratio transmitter of its counterpart to align their signals at the relay. We then analyze the spectral efficiency of the designed SA signaling, compare it with non-SA (NSA) signaling, and optimize the power allocation among the relay and users. It is shown from asymptotic analysis and simulation results that the proposed SA signaling can support more users and achieve higher spectral efficiency than NSA signaling.

In this contribution we investigate the spectral-efficiency of a two-hop cooperative network using multicarrier code-division multiple-access (MC-CDMA) transmission scheme. The two-hop network constitutes K source users transmitting signals to K destinations with the aid of N relays. Our focus is on the relay optimization, when assuming that the N relays cooperate or do not cooperate with each other. Specifically, in this contribution the egocentric-altruistic (E-A) optimization is introduced, which constitutes an E-optimization motivating to suppress the multiuser interference (MUI) of the source-relay channels and an A-optimization aiming to pre-mitigate the potential MUI of the relay-destination channels. Both the minimum mean-square error (MMSE) and zero-forcing (ZF) optimization criteria are considered. Furthermore, the spectral-efficiency performance of the two-hop MC-CDMA systems using the proposed E-A relay optimization is investigated by simulations, when assuming communications over frequency-selective fading channels.

This letter aims to design a low-complexity subcarrier-power allocation scheme to improve the communication reliability of various types of frequency-division multiple-access (FDMA) systems. Both uplink and downlink are considered. Specifically, a low-complexity worst subcarrier avoiding (WSA) subcarrier-allocation scheme is proposed, in order to avoid assigning users the subcarriers experiencing severe fading. After the subcarrier-allocation, channel-inversion assisted powerallocation is employed to assign the subcarriers the corresponding power. Our studies and simulation results show that the achievable error performance of the FDMA systems employing the proposed subcarrier-power allocation algorithm is independent of the multiplexing method. The proposed algorithm outperforms the existing subcarrier-power allocation algorithms that have a similar complexity as the proposed one.

This contribution considers the joint transmitter-receiver optimization in generalized multicarrier code-division multiplexing (GMCCDM) systems. The joint transmitter-receiver optimization consists of an one-tap post-frequency-domain equalizer (post-FDE) and an one-tap pre-FDE. The one-tap post-FDE is optimized under the criterion of minimum mean-square error (MMSE), while the one-tap pre-FDE is optimized in order to achieve either the highest possible throughput or best possible error performance. Furthermore, an optimum spreading scheme is proposed for the GMC-CDM system, which is capable of achieving certain signal-to-noise ratio (SNR) gain in addition to the promised close-loop diversity gain. In this paper the performance of the GMC-CDM systems is investigated, when communicating over frequency-selective Rayleigh fading channels. It can be shown that our proposed pre-FDE and post-FDE algorithms are highly efficient, which are low-complexity and capable of enhancing significantly the error or throughput performance of the GMCCDM systems.

Design of equalizers and spread-spectrum modes or code allocation schemes is crucial for achieving good performance multi-code multiple access systems. In this paper, a symbol- level frequency domain equalizer and spread-spectrum mode are jointly designed to maximize the diversity gain with low complexity. Since the proposed transceiver scheme is independent of the orthogonal codes, it can be applied to direct sequence code division multiple access (DS-CDMA), multi-carrier CDMA (MC-CDMA) and orthogonal frequency division multiple access (OFDMA) systems. Simulation results are provided which validate the theoretical analysis.

The major challenges confronted in the timing acquisition for ultra-wideband (UWB) communication systems are enormous search space, extremely low signal power and the heavy multiple access interference (MAI) environments. Since the acquisition methods with the conventional preamble structure suffer severe performance degradation in the presence of interference, the authors present a novel preamble structure in this paper where the preambles for different users have unequal symbol durations. The proposed structure associated with any acquisition algorithm can improve the accuracy of timing estimation and shorten the acquisition time significantly for multi-user systems, which is demonstrated by both theoretical analyses and simulation results.