High-mobility and large-bandwidth applications at high frequencies make the acquisition of doubly-selective channels costly and complex, as the fast fading channel causes a surge in pilot overheads and inter-carrier interference. Accurate estimation of the complex channel gain (CG) and the carrier frequency offset (CFO) is required to support subsequent high-accuracy channel prediction that alleviates the heavy pilot burden. The emerging technology of the wireless channel map (WCM) can approximately reproduce the actual propagation environment digitally with customized channel parameters, offering an opportunity to accurately estimate the complex CG and CFO. In this paper, a WCM-based prior distribution construction method and a joint complex CG and CFO estimation algorithm are proposed. Specifically, a parameterized linear estimation problem for the complex CG is generated based on a nonuniform delay-domain off-grid channel representation, and with more realistic prior distributions constructed by the knowledge from the WCM-provided angular-delay power spectrum density, the joint estimation problem is solved under the Bayesian inference framework. Simulation results demonstrate the superiority of the proposed algorithm, with a better performance in terms of estimation accuracy and bit error rates (BER) than existing baselines. It is also verified that the proposed WCM-based algorithm is highly adaptable to different WCM precision and robust to different user speeds.

As a key paradigm in sixth-generation (6G) systems for enabling physical–virtual mapping and intelligent network management, Digital Twin (DT) networks demand high-precision, low-latency modeling and continuous updates of wireless environments. However, in dynamic urban scenarios, channel conditions are highly non-stationary, and traditional sensing schemes suffer from limitations in coverage, information timeliness, and scheduling efficiency. To address these challenges, this paper proposes an Integrated Communication-Computing-Intelligence-Controlling (ICCIC) network based on layered unmanned aerial vehicles (UAVs). The system comprises mobile edge servers (ESs) with trajectory control and scheduling capabilities, and statically deployed working UAVs responsible for wireless channel measurements. The collected data is offloaded to ESs for edge computing and local model construction, which supports subsequent DT channel modeling. A multi-objective optimization problem is formulated to jointly schedule UAV trajectories and data offloading, with Age of Information (AoI) and service delay as performance metrics. The optimization is subject to constraints including maximum task execution time and service fairness. To capture multi-type interactions among UAVs during sensing and coordination, we build a multi-relational heterogeneous graph and encode it with a graph neural network (GNN) to obtain structured system states. On this basis, we adopt a QMIX-based multi-agent reinforcement learning (MARL) framework under centralized training and decentralized execution (CTDE) is developed to learn cooperative scheduling policies. Simulation results demonstrate that the proposed method achieves superior performance in sensing timeliness, scheduling responsiveness, and policy convergence, providing robust support for efficient and reliable DT channel modeling.

With over 80% of mobile traffic occurring indoors, optimizing indoor wireless communication performance is crucial. For indoor communication systems, interior walls play a significant role in shaping system performance, yet their impacts on wireless communication performance at the link level remain insufficiently explored. In this paper, we propose an evaluation method for indoor wireless performance, considering wall materials and locations. Based on the evaluation method, the relationships between interior walls and signal propagation are investigated, considering line-of-sight (LOS) probability, blockage height, blockage density, and wall materials. The signal propagations are divided into four cases, including the direct path, the reflection path and/or penetration path through walls, and the multipath from scatterers. Moreover, spatial cross-correlation function (SCCF), spectrum efficiency (SE), and energy efficiency (EE) are analyzed to unveil the impacts of walls on channel characteristics and system performance. Our findings reveal that strategically adjusting the placement and materials of walls, while keeping network settings unchanged, can significantly enhance system performance.

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) provides an almost uniformly high data rate for all user equipment (UE) through multiple access points (APs), with a non-negligible signal processing burden. Angle domain transmission and channel maps promise to alleviate this burden by reducing channel dimensions in the angle domain and providing$a$priori channel information, respectively. In this paper, we propose a channel map-based angle domain multiple access scheme for uplink CF M-MIMO communications. First, we propose a twostage data reception and pilot assignment scheme constituting receive combining and large-scale fading decoding (LSFD) to reduce overall interference and maximize spectral efficiency (SE). Furthermore, we construct two channel map-based transmission mechanisms by wielding different levels of channel information, where a tailored data reception scheme with a newly derived SE upper bound is also proposed for quantitative evaluation. Simulation results show that the proposed schemes outperform both their space domain alternatives and those without using channel maps in terms of SE.

The explosive growth of the sixth-generation ( 6 G ) wireless system necessitates efficient base station (BS) deployment to balance coverage, data rates, and economic costs. In this paper, we propose a novel BS deployment scheme that jointly optimizes the number and locations of the deployed BSs, considering limited BS throughputs and practical non-uniform user distributions. A variant multi-dimensional knapsack problem is formulated and then solved by proposing a novel depth-limited backtracking dynamic programming (DLB-DP) algorithm with a BS-user association (BSUA) algorithm. We compare the proposed DLB-DP algorithm with three state-of-the-art benchmark algorithms in a hotspot scenario. The results demonstrate that the proposed algorithm outperforms the considered alternatives in terms of coverage, data rates, and robustness under varying user densities.

In this paper, we study the impacts of user mobility on the average bit error probability (ABEP) and average achievable data rate (ADR) of ultra-reliable and low-latency communication (uRLLC) systems. Specifically, based on the time autocorrelation function (ACF) of the sixth generation pervasive channel model (6GPCM), we derive the expression of the pairwise error probability (PEP) of uRLLC systems adopting the M-ary phase shift keying (MPSK) modulation. In addition, we propose a new framework for the calculation of the average ADR considering the channel estimation period and cyclic prefix (CP) length. Moreover, the calculation methods of maximal data transmission period and transmission latency are given. Numerical results show that the increasing user mobility degrades the ABEP and average ADR. Impacts of user mobility on the ABEP surpass its influences on the average ADR. Furthermore, the tightness of analytical results is validated by Monte Carlo simulations. The performance analysis will guide the flexible designs of transmission blocklength under different reliability constraints.

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2–4 times higher thanks to the reduced processing and signaling.

How to manage the interference introduced by the enormous wireless devices is a crucial issue to address in the prospective sixth-generation (6G) communications. The treating interference as noise (TIN) optimality conditions are commonly used for interference management and thus attract significant interest in existing wireless systems. Cell-free massive multiple-input multiple-output (CF mMIMO) is a promising technology in 6G that exhibits high system throughput and excellent interference management by exploiting a large number of access points (APs) to serve the users collaboratively. In this paper, we take the first step on studying TIN in CF mMIMO systems from a stochastic geometry perspective by investigating the probability that the TIN conditions hold with spatially distributed network nodes. We propose a novel analytical framework for TIN in a CF mMIMO system with both Binomial Point Process (BPP) and Poisson Point Process (PPP) approximations. We derive the probability that the TIN conditions hold in close form using the PPP approximation. Numerical results validate our derived expressions and illustrate the impact of various system parameters on the probability that the TIN conditions hold.

Caching popular contents at a large number of access points and edge-clouds is a promising solution to alleviate the increasing backhaul congestion in beyond fifth-generation (B5G) networks. By integrating with cell-free massive multiple-input multiple-output (CF mMIMO), wireless caching can harness their combined virtues, i.e., almost uniform service quality, strong macro-diversity, and reduction of the data traffic from the core network. In this paper, we consider an offline cache-aided scenario with two caching strategies to minimize the total energy consumption (TEC), which are evaluated from the cache hit probability (CHP). The TEC minimization is showed to be NP-complete and, hence, dealt with a proposed greedy algorithm. An adaptive power control policy is proposed to reduce the TEC. We compare CF mMIMO with small cells in terms of the successful content delivery probability (SCDP) and TEC, respectively. The numerical results show that CF mMIMO can offer a much more uniform service, significantly higher SCDP, and lower average TEC when compared to than SC.

How to meet the demand for increasing number of users, higher data rates, and stringent quality-of-service (QoS) in the beyond fifth-generation (B5G) networks? Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising solution, in which many wireless access points cooperate to jointly serve the users by exploiting coherent signal processing. However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital. In this paper, we propose a new framework for structured massive access in cell-free massive MIMO systems, which comprises one initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and one fractional power control policy. New closed-form spectral efficiency (SE) expressions with maximum ratio (MR) combining are derived. The simulation results show that our proposed framework provides high SE when using local partial minimum mean-square error (LP-MMSE) and MR combining. Specifically, the proposed initial access algorithm and pilot assignment schemes outperform their corresponding benchmarks, P-LSFD achieves scalability with a negligible performance loss compared to the conventional optimal large-scale fading decoding (LSFD), and scalable fractional power control provides a controllable trade-off between user fairness and the average SE.

To reduce the hardware cost and power consumption in millimeter wave (mmWave) multiuser massive multiple-input multiple-output (MIMO) systems, a quantized hybrid transmit model with low-resolution digital-to-analog converters (DACs) is introduced. A correlation-based hybrid precoding algorithm is proposed as an attractive low-complexity approach for the considered system. Using the Bussgang theorem and the additive quantization noise model (AQNM), we derive the asymptotic downlink achievable rate expressions. The rate loss caused by low-resolution DACs shows negligible quantization distortion at low power regime. Numerical results demonstrate the superiority of the proposed algorithm over other algorithms in terms of the rate performance.

Cell-free massive multiple-input multiple-output (MIMO) is a promising network architecture for future wireless systems. This paper investigates the uplink performance of cell-free massive MIMO systems employing the least-square (LS) estimator over spatially correlated fading channels. We first derive a generalized closed-form expression of the spectral efficiency as a function of the number of access point (AP) antennas and the spatial correlation matrices. We use this result to analyze the impact that the fronthaul, number of users and number of APs have on the energy efficiency. Compared to traditional co-located massive MIMO using maximum ratio combining (MRC), our analysis shows that the large performance gain of cell-free massive MIMO with low-complexity linear LS estimators.

High-speed railway (HSR) wireless communications are required to support high data rate with seamless connectivity. In this paper, we investigate the outage performance of a downlink single-cell non-orthogonal multiple access (NOMA) based wireless network in HSR scenarios, where it is challenging to derive the perfect channel state information (CSI) and the distribution of the users are quite different from the traditional cellular scenarios. More specifically, the performance of NOMA over composite large-scale and Rician fading channel is studied with two kinds of partial CSI, e.g., imperfect small-scale CSI and no small-scale CSI. We derive the exact closed-form expression of the outage probability based on partial CSI, respectively, by using the Gauss-Chebyshev quadrature method. Finally, simulation results evidence the validity of the derived results and show that the average outage probability of NOMA systems outperforms conventional orthogonal multiple access systems.