Interference cancellation (IC) is crucial for sustaining high-performance massive MIMO downlink receivers at cell edges, where capacity is severely limited by strong co-channel interference. Achieving Bayes-optimal recovery of this interference is fundamentally challenging, as it typically requires explicit prior knowledge of the interfering modulation formats. To overcome this limitation, we propose a novel, modulation-agnostic interference estimation approach that unfolds the Approximate Message Passing (AMP) framework, replacing its standard static denoiser with a dynamic Empirical Bayes (EB) component. In this synergistic architecture, AMP systematically decouples the spatial MIMO channel to provide the scalar environment required by EB, while EB adaptively learns the prior distribution and infers the signal without any pre-existing modulation knowledge. This approach completely circumvents the need for brute-force exhaustive searches, rendering it significantly more efficient than conventional model selection methods, while exhibiting superior computational scalability compared to Expectation-Maximization (EM) based AMP. We integrate this EB-AMP estimator into a comprehensive joint detection framework that iteratively alternates among data detection, signal subtraction, interference recovery, and interference cancellation. Numerical simulations demonstrate that under the assumptions of perfect channel state information (CSI), i.i.d. Rayleigh fading, and a single interfering cell, the proposed IC receiver effectively suppresses unknown interference at power levels comparable to the desired data signal, achieving an estimation accuracy that closely approaches the theoretical interference-free bound.

This letter explicitly models a coarse and noisy quantization in a communication system empowered by orthogonal time frequency space (OTFS) for cost and power efficiency. We first point out, with coarse quantization, the effective channel is imbalanced and thus no longer able to circularly shift the transmitted symbols along the delay-Doppler domain. Meanwhile, the effective channel is non-isotropic, which imposes a significant loss to symbol detection algorithms like the original approximate message passing. Although the algorithm of generalized expectation consistent for signal recovery (GEC-SR) can mitigate this loss, the complexity in computation is prohibitively high, mainly due to an dramatic increase in the matrix size of OTFS. In this context, we propose a low-complexity algorithm that embed into GEC-SR a quick inversion of the quasi-banded matrices, thus reducing the algorithm's complexity from cubic order to linear order, while keeping the performance at almost the same level.

This paper studies the joint estimation of channel fading and user activity for the uplink of a grant-free massive machine-type communication system. Comparing with previous studies, we consider more practical aspects of the system, including non-i.i.d. signature matrices, low-resolution quantization, and random users activated by an unknown sparse rate. A new estimation algorithm, termed hybrid decentralized generalized expectation consistent (HyDeGEC), is then derived based on a hybrid network that applies scalar message passing for the prior inference and vector message passing for the likelihood inference. This new algorithm outperforms many state-of-the-art techniques in terms of robustness (to non-i.i.d. signatures), complexity (in computation per iteration), and/or estimation accuracy (of the channel and the activity rate). The state evolution of the algorithm is also analyzed, which, as validated by simulations, can capture precisely the algorithm’s per-iteration behavior in MSE. Summing up, the algorithm we propose here is practically effective, computationally efficient, and theoretically analyzable.

The reconstruction of sparse signal is an active area of research. Different from a typical i.i.d. assumption, this paper considers a non-independent prior of group structure. For this more practical setup, we propose EM-aided HyGEC, a new algorithm to address the stability issue and the hyper-parameter issue facing the other algorithms. The instability problem results from the ill condition of the transform matrix, while the unavailability of the hyper-parameters is a ground truth that their values are not known beforehand. The proposed algorithm is built on the paradigm of HyGAMP (proposed by Rangan et al.) but we replace its inner engine, the GAMP, by a matrix-insensitive alternative, the GEC, so that the first issue is solved. For the second issue, we take expectation-maximization as an outer loop, and together with the inner engine HyGEC, we learn the value of the hyper-parameters. Effectiveness of the proposed algorithm is also verified by means of numerical simulations.

This letter considers the problem of a joint estimation for channel fading and user activity in an uplink grant-free massive MIMO system equipped with low-precision analog-to-digital converters (ADCs). Different from existing works, the joint estimation is formalized as a non-overlapping group problem, where the components of compound channel involving user activity indicator and channel fading are independent with condition distribution rather than independent Bernoulli-Gaussian. Based on this new formulation, a new algorithm leveraging hybrid generalized approximate passing (HyGAMP) is then developed including GAMP part (channel estimation) and loopy belief propagation (LBP) part (user activity detection), where the strong correlation among elements in each row of the channel matrix can be decoupled in LBP part. By exchanging the information between the GAMP part and the LBP part, the proposed algorithm improves the performance of channel estimation and user activity detection as compared to earlier results. In addition, the simulation results verify that the proposed algorithm develops the performance of conventional methods dramatically.

This letter considers a joint user activity, channel and data estimation problem in an uplink grant-free massive MIMO systems with low-precision analog-to-digital converters (ADCs). This joint estimation is firstly formalized as a non-overlapping group sparse problem, in which the components of compound channel follow independent conditional distribution. To address this problem, a new algorithm consists of the celebrated bilinear generalized approximate message passing (BiG-AMP) and loop belief propagation (LBP) is then proposed, where the strong connection of compound channel can be decoupled in LBP part. By exchanging the information between BiG-AMP part and LBP part, the proposed algorithm improves the performance of channel estimation compared with HyGAMP based method, in which the estimated payload data of proposed algorithm are utilized to aid channel estimation and it leads to relatively few pilot symbols to achieve equivalent channel and data estimation performances. The simulation results confirm that our proposed joint estimation algorithm improves the performance of the existing works in terms of user activity, channel and data estimation.

Generalized approximate message passing (GAMP) is an efficient algorithm for the estimation of independent identically distributed random signals under generalized linear model. The sum-product GAMP has long been recognized as an approximate implementation of the sum-product loopy belief propagation. In this letter, we propose to view the message passing in a new perspective of expectation propagation (EP). Comparing with the previous methods that were based on Taylor expansions, the proposed EP method could unify the derivations for the real and the complex GAMP, with a difference only in the setup of Gaussian densities.

The limited lifetime of wireless nodes has become the essential bottleneck of system performance and wide-scale deployment of wireless networks. In this paper, we consider a practical mobile chargeable network in which each single mobile charger is able to charge multiple target nodes simultaneously. To tackle the problem, the cooperative grouping of the wireless nodes is proposed to reduce the number of traversing spots of the mobile chargers. Meanwhile, the cooperative charging among the mobile chargers is studied to conserve their energy consumption. The numerical results show that the proposed scheme outperforms existing strategies in both even-density and uneven-density wireless networks.

Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and grid, which provides powerful demand response, balancing the electricity demand and supply in smart grid. Mobility is the key feature of EVs, which is also a significant challenge for V2G systems. In order to model the EV mobility in V2G systems, we propose a complex networking modeling for V2G mobile energy network. Each district has a V2G system. EVs travel among different districts. The EV fleets transport energy and impact the V2G systems of districts. The theory of complex network synchronization is employed to analyze the dynamics of the mobile energy network. Numerical results show the energy transportation of EV fleets may achieve synchronous stability of demand level of different districts, balancing the demand response in the mobile energy network.

This paper considers the multiuser relay network, which applies Laneman's seminal protocol of selective decode and-forward relaying. We mainly consider the opportunistic scheduling scheme which selects the user who has the largest received signal-to-noise ratio at the destination for transmission. We derive the exact and asymptotic expressions for the outage probability under Rayleigh fading, and the diversity analysis reveals that a total diversity order of K + 1 can be achieved, where K is the number of users. Besides, we propose a new multiuser scheduling scheme which achieves the same outage performance and improves the fairness among users. Finally, numerical simulations are presented to validate the analytical results.

This paper investigates the symbol error rate (SER) performance of the multiple-input multiple-output (MIMO) multi-channel beamforming (MB) in the general double-scattering channel. We derive an asymptotic expansion on the marginal eigenvalue distribution of the MIMO channel matrix, and apply the result to get an approximate expression on the average SER at high signal-to-noise ratio (SNR). Two parameters pertaining to the SER, i.e., the diversity gain and the array gain, are analyzed. Our results show that it suffices for the double-scattering channel to have only limited scatterers, if the same diversity gain as the Rayleigh channel is desired; however, once the number of scatterers is below a certain level, the array gain in the double-scattering channel will vary with the SNR logarithmically.

The paper establishes connections between orthogonal space-time block codes (OSTBC) and multiple-input multiple-output beamforming (MIMO BF). Different from previous work, where the two schemes were studied independently and under specific fading conditions, we consider a framework which unifies the analysis of both schemes in arbitrary fading. In particular, we demonstrate that the diversity gains of OSTBC and MIMO BF are equivalent, regardless of the underlying channel fading distribution. We also derive bounds on the difference in transmit power required for the two schemes to achieve the same outage probability. This result, which again applies for arbitrary fading, quantifies the gain of MIMO BF over OSTBC due to the presence of transmitter channel knowledge.

In this paper, we investigate the performance of multiple-input multiple-output (MIMO) multi-channel beamforming (MB) systems in double-scattering channels. In particular, we first derive new expressions for the marginal ordered eigen-value distributions of the double-scattering channel matrices. Based on these results, we present analytical expressions for the symbol error rate of MIMO MB. It is demonstrated that MB sub-channels can achieve full spatial diversity even in the presence of double scattering, if the number of effective scatterers is greater than or equal to the maximum number of transmit and receive antennas.

The complex central semi-correlatedWishart matrix is the product of a zero-mean row-wise correlated (or column-wise correlated) complex Gaussian matrix and its Hermitian transposition. In this paper, we investigate the distribution of the smallest eigenvalue of the Wishart matrix, which is generated from the square Gaussian matrix. A closed-form expression for distribution of the smallest eigenvalue is derived. Compared with previous results, our expression is very concise, since the distribution has been known in terms of matrix determinants. Numerical results that confirm the expression are also presented. And a complicated equality is proved due to the fact that our distribution is mathematically equivalent to those reported.

Wishart matrix and its eigenvalues are very important to the analysis of multiple-antenna systems. In this paper, we derive a polynomial expression for the distribution of the smallest eigenvalue of Wishart matrices. Compared with earlier results in matrix determinant forms, our expression is much easier to handle. The expression obtained applies for Wishart matrices of arbitrary orders and is an extension to Edelman's and Park's results. Moreover, our work here can also be viewed as an interesting proof of one complicated equality, since the distribution is unique and our result must be equivalent to earlier researchers'. In a word, with the result of this paper, further mathematical analysis on the statistics of the smallest eigenvalue becomes possible.