A novel semi-orthogonal transmission method, the pilot data superimposed dynamic inflow (PDSDI) method, is proposed to tackle the challenges of massive access in grant-free random access wireless communication systems. This approach aims to balance large-scale user access while mitigating performance degradation associated with non-orthogonal pilot sequences. The traditional orthogonal transmission scheme is also analyzed as a baseline for comparison. Uplink rate expressions in closed form are obtained under two common channel estimation methods, that is, least squares and minimum mean square error. The analysis investigates the asymptotic behavior of important system parameters, including RiceanK-factor, antenna numbersM, and symbol power, revealing that the sum uplink rate converges to log2M, regardless of theK-factor. In addition, a parameter optimization model is developed to maximize system performance by adjusting critical variables, such as antenna number and power scaling factors, ensuring improved system capacity and efficiency. The simulation results confirm that the PDSDI method greatly exceeds the performance of the traditional scheme with respect to uplink rate and user access capacity. This work presents a practical framework for optimizing system parameters in future large-scale wireless networks.

In unmanned aerial vehicle (UAV) communications, beam tracking faces significant challenges due to persistent variations in mobile users’ positions and flight attitudes. Fixed tracking periods result in either unnecessary or insufficient overhead depending on whether the UAV’s relative motion is static or dynamic. To address these challenges, this paper proposes a two-level beam tracking scheme enabled by data-driven beam prediction. In the upper layer, a time-series prediction method is used to adaptively adjust the tracking interval by forecasting confidence intervals of future communication quality. In the lower layer, a threshold-based probing beam selection method is implemented, which selects probing beams whose conditional likelihoods exceeding a predefined threshold. Simulation results demonstrate that the total beam training overhead drop 43.37 %, while the average outage probability decreases from 6.229 % to 0.095 %.

The spectral efficiency in cell-free massive MIMO with grant-free random access under Ricean fading, utilizing a linear maximal-ratio combining detector, is the focus of this paper. Firstly, explicit expressions for the effective signal-to-interference-plus-noise ratio (SINR) are introduced, using the least squares (LS) and minimum mean squared error (MMSE) estimation methods, valid for varying Ricean K-factor and for varying numbers of access point antennas M. Furthermore, we drive the asymptotic SINR expressions under extreme conditions of infinite Ricean K-factor, infinite M, and the appropriate scaling of the users’ power. The corresponding analysis shows that as M approaches infinity, the sum uplink rate converges to $\log _{2} M$, regardless of the Ricean K-factor. Particularly, in the case of LS estimation, reducing each user’s transmit power by $1 / \sqrt{M}$ maintains the rate, converging to a definitive value irrespective of the Ricean K-factor. In the case of MMSE estimation, power is able to be scaled down by $1 / \sqrt{M}$ to achieve it when Ricean K-factor is zero, whereas the power of nonzero Ricean K-factor necessitates a reduction by $1 / M$. Finally, simulations confirm all theoretical outcomes.

More recently, the cell-free massive multiple-input multiple-output (CF -mMIMO) has gained much attention as it can significantly reduce the path loss and improve the user experience. However, the application of CF-mMIMO is limited by the payload and delays caused by channel state information (CSI) exchange among access points (APs). To solve this problem, we use the distributed learning (DL) framework to design a fully local receiver. We set up a distributed deep neural network (DNN) model at each AP that takes the local estimated CSI as input, labeled by the centralized minimum mean square error (MMSE) receiver. As no CSI exchange is needed, the AP can produce a powerful local receiver without any payload and delays on the fronthaul during the whole process. Simulation results have shown that the DL-aided receiver can significantly improve the spectral efficiency over traditional local receivers. Moreover, as we take the large-scale fading into account and apply appropriate hyperparameters to accelerate the model convergence, our model can converge fast and be available for both fixed and moving users.

Grant-free random access (GFRA) becomes attractive in Internet of Things (IoT) due to its low signaling overhead and short access latency. In this article, we investigate GFRA in a multicel massive multiple-input-multiple-output (MIMO) system. As the IoT device usually has sporadic traffic, we set a packet buffer for each device to describe its temporal traffic, and also use the stochastic geometry to describe the randomness of devices’ spatial locations. With the backoff mechanism, only devices with a nonempty buffer and a successful backoff are activated and allowed to request access. Unlike previous works that regard all devices selecting the same pilot (i.e., the colliding devices) as undetectable, we give a more accurate model that the base station (BS) can detect colliding devices when they locate far away from each other, and we set a unique collision area for each device to quantify the boundary that the collision can be ignored. A tight approximation for the number of packets successfully transmitted at the unit area and time slot, named packet throughput, is derived. Based on it, we obtain the optimal backoff parameter that maximizes the packet throughput under devices’ delay constraints. It is shown that when pilots are insufficient or device packet traffic is heavy, a long backoff time is needed. However, as the pilot grows or the packet traffic turns light, devices should gradually reduce the backoff time. In particular, if pilots are surplus, cheap detectors can be equipped on the BS without an obvious packet throughput reduction.

Grant-free random access (GFRA) becomes attractive in Internet-of-Things (IoT) due to its low signaling overhead. In this paper, we investigate the GFRA in a multicell massive multiple-input multiple-output (MIMO) system after considering both the spatial and temporal traffic of devices. By introducing the backoff mechanism, only devices with non-empty buffer and a successful backoff can request access. Unlike previous works on GFRA that regard all devices selecting the same pilot as undetectable, we set a unique collision area for each device to quantify the boundary that BS can detect the collision. With tools of stochastic geometry and queueing theory, we derive a tight approximation for the number of packets successfully transmitted at unit area and time slot, named as packet throughput (Tp). Based on it, we find that the range of the collision area has a remarkable effect on Tp. The optimal backoff parameter that maximizes Tpis also obtained, and we find that a long backoff time is needed when the pilot is insufficient or the packet traffic is heavy. Compared with the fully-loaded access, our optimal backoff mechanism can significantly improve Tp, especially for the system with crowded devices.

With the deluge of wireless data, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial small base stations (SBSs) to relieve the load of ground macro base stations by establishing wireless backhaul connections with them and providing high-quality service to users. Thanks to the emergence of integrated access and backhaul (IAB), the access and backhaul communication links can work on the same millimeter wave (mmWave) band with huge available bandwidth. This paper studies the quality-of-service (QoS) performance of heterogeneous traffic under equal partition and average load partition spectrum allocation strategies for mmWave UAV-assisted IAB cellular networks. Specifically, we develop a theoretical framework to analyze the mean packet throughput (MPT) of users based on stochastic geometry and queueing theory. Simulation results demonstrate that the deployment of UAVs can promote MPT performance compared to ground SBSs and appropriate UAV height, UAV density, and spectrum allocation play significant roles in improving QoS performance of heterogeneous traffic in the network.

In this article, we develop a framework for the analysis of massive multiple-input-multiple-output (MIMO) systems where multiple types of devices with different configurations and requirements co-exist, by taking into account the randomness of spatial locations and temporal traffic. A tight closed-form approximation of the spatial mean packet throughput, which denotes the average number of packets that are successfully transmitted at any unit time slot and area is derived, by using tools from the stochastic geometry and queuing theory, which captures all the key features of the devices in the Internet of Things (IoT). Based on the analysis, we investigate the optimal scheduling number for each type of devices that maximizes the spatial mean packet throughput while meeting devices' delay constraints. It is found that when the base station (BS) has excessive number of antennas ( M), the BS should schedule all devices under its coverage, regardless of devices' variances on spatiotemporal configurations and demands. However, when M is limited, the BS should have a bias on scheduling devices with heavier traffic, lower decoding threshold, or higher transmit power. On this basis, if the delay constraint of one device becomes stricter, it will be scheduled more often to access the radio channel, which acts more significantly when the ratio of M to the deployment density of devices gets smaller.

In this paper, we consider the channel estimation and multi-user detection problems in fog radio access networks (F-RANs). Based on block coordinate descent algorithm, we propose two methods to solve a mixed ℓ2,1-regularization functional which exploits both the sparsity of user activities and the spatial sparsity of user signals in F-RAN. Both of our methods split the computation and corresponding data into multiple units of a cluster and solve the problem in a distributed manner. Hence they can be deployed flexibly at the distributed logical edges as well as the cloud baseband unit pool in F-RAN. The differences between the two methods are that the first one operates in a serial manner and is guaranteed to converge, while the second one works in parallel and under empirical guidance. Deployment details are also provided. Numerical results demonstrate the effectiveness of the proposed methods.

This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). By taking into account of the sparsity of user activities in C-RAN, we solve the CE and MUD problems with compressed sensing to greatly reduce the large pilot overhead. A mixed ℓ2,1-regularization penalty functional is proposed to exploit the inherent sparsity existing in both the user activities and remote radio heads with which active users are associated. An iteratively re-weighted strategy is adopted to further enhance the estimation accuracy, and empirical and theoretical guidelines are also provided to assist in choosing tuning parameters. To speed up the optimization procedure, three low-complexity methods under different computing setups are proposed to provide differentiated services. With a centralized setting at the baseband unit pool, we propose a sequential method based on block coordinate descent (BCD). With a modern distributed computing setup, we propose two parallel methods based on alternating direction method of multipliers (ADMM) and hybrid BCD (HBCD), respectively. Specifically, the ADMM is guaranteed to converge but has a high computational complexity, while the HBCD has low complexity but works under empirical guidance. Numerical results are provided to verify the effectiveness of the proposed functional and methods.

In this paper, we propose a framework for rate analysis in the multicell massive multiple-input multiple-output (MIMO) system which models the channel within one cell as Rician fading and considers different kinds of hardware impairments. Closed-form achievable uplink rates with common and separate oscillators are derived, respectively. Based on them, we find that line-of-sight (LoS) components can benefit the rate performance under any configurations, but this improvement decays as the phase drift aggravates. Utilizing the excessive degrees-of-freedom offered by massive MIMO, we can tolerate the hardware constraints without rate reduction. In particular, as the number of base station (BS) antennas (M) grows, we can relax the hardware constraint on additive noise by M in LoS-Rayleigh mixed channel, which is different from the relaxation by √M in pure Rayleigh channel, whereas the hardware constraint on phase drift can only be relaxed logarithmically with M. Furthermore, we also utilize the benefit of LoS components to reduce the required antenna size and transmit powers. Via simulation, we find that with sufficient LoS propagations, both the BS antenna and transmit powers can be cut down remarkably while maintaining a non-reduction rate performance as the Rayleigh fading environment, which is useful for the ultra-dense network in future.

This paper proposes a low-cost distributed massive multiple-input multiple-output (MIMO) system, which employs the mixed analog-to-digital converter (ADC) remote radio heads (RRHs). In particular, the RRHs with low-resolution ADCs connect with the baseband unit through wireless fronthaul while the RRHs with full-resolution ADCs connect with the baseband unit through fiber. After estimating the channel state information between RRHs and users, we derive the closed-form expressions for achievable downlink rate and energy efficiency. Based on these analytical results, we find that our distributed architecture can obtain remarkable gains compared with the centralized layout. Moreover, compared with the conventional distributed network with pure expensive full-resolution ADCs, our mixed-ADC architecture can achieve the rate requirement in a more energy-efficient and low-cost way, especially for the low rate demands. Additionally, we also present the optimal RRH assignment proportion that can maximize the energy efficiency under a fixed total number of RRHs, which can be used as guidelines for practical network configuration.

We develop a framework for downlink heteroge- neous cellular networks with line-of-sight (LoS) and non-line- of- sight (NLoS) transmissions. Using stochastic geometry, we derive a tight approximation of the achievable downlink rate that enables us to compare the performance between densifying small cells and expanding base station (BS) antenna arrays. Interestingly, we find that adding small cells into a sparse network improves the achievable rate much faster than expanding antenna arrays at the macro BS. However, when the small cell density exceeds a critical threshold, the spacial densification will lose its benefits and further impair the network capacity. To this end, we present the optimal small cell density that maximizes the rate as practical deployment guidance. In contrast, expanding macro BS antenna array can always increase the capacity until reaching an upper bound caused by pilot contamination, and this bound also surpasses the peak rate obtained from deployment of small cells. Therefore, small cells are preferred for low rate requirements due to the rapid rate gain, and the massive MIMO is preferred for higher rate requirements due to better achievable rate.

In this paper, we propose a relative channel estimation error (RCEE) metric, and derive closed-form expressions for its expectation Exprcee and the achievable uplink rate holding for any number of base station antennas M, with the least squares (LS) and minimum mean squared error (MMSE) methods. It is found that RCEE and Exprcee converge to the same constant value when M → ∞, which renders the pilot power allocation (PPA) substantially simplified and a PPA algorithm is proposed to minimize the average Exprcee per user under a total pilot power budget F in multi-cell massive multipleinput multiple-output systems. Numerical results show that the PPA algorithm brings considerable gains for the LS estimation compared with equal PPA (EPPA), while the gains are significant only with large frequency reuse factor (FRF) for the MMSE estimation. Moreover, for large FRF and large F, the performance of the LS approaches to that of the MMSE. Besides, a scheduling strategy is proposed to allocate pilot power in the whole system, which can approach the optimal performance. For the achievable uplink rate, the PPA scheme and improves the minimum achievable uplink rate compared with the EPPA scheme.

We develop a framework for downlink heterogeneous cellular networks with line-of-sight (LoS) and non-LoS transmissions. Using stochastic geometry, we derive tight approximation of average downlink rate that enables us to compare the performance between densifying small cells and expanding base station (BS) antenna arrays. Interestingly, we find that adding small cells into the network improves the downlink rate much faster than expanding antenna arrays at the macro BS. However, when the small cell density exceeds a critical threshold, the spatial densification will lose its benefits and further impair the network capacity. To this end, we provide the optimal small cell density that maximizes the rate via numerical results for practical deployment guidance. In contrast, expanding macro BS antenna array can always benefit the capacity until an upper bound caused by pilot contamination, and this bound also surpasses the peak rate obtained from the deployment of small cells. Furthermore, we find that allocating part of antennas to distributed small cell BSs works better than centralizing all antennas at the macro BS, and the optimal allocation proportion is also given numerically for practical configuration reference. In summary, this paper provides a further understanding on how to leverage small cells and massive MIMO in future heterogeneous cellular networks deployment.

We investigate the achievable uplink rate of multi-cell massive multi-input multi-output (MIMO) systems considering pilot reuse across cells. Based on tractable approximations for the achievable rates, we derive the optimal pilot length to maximize the sum rate per cell. Interestingly, it is found that, for moderately large numbers of base station (BS) antennas, the optimal pilot length is above the minimum feasible value, and the gains brought by the extra amount of training diminish as the reuse factor grows. Simulation results confirm the accuracy of our analysis.

This paper investigates the sum-rate gains brought by power allocation strategies in multicell massive multiple-input- multiple-output systems, assuming time-division duplex transmission. For both uplink and downlink, we derive tractable expressions for the achievable rate with zero-forcing receivers and precoders, respectively. To avoid high-complexity joint optimization across the network, we propose a scheduling mechanism for power allocation, where, in a single time slot, only cells that do not interfere with each other adjust their transmit powers. Based on this, corresponding transmit power allocation strategies are derived, aimed at maximizing the sum rate per cell. These schemes are shown to bring considerable gains over equal power allocation for practical antenna configurations (e.g., up to a few hundred). However, with fixed number of users N, these gains diminish as M → ∞, and equal power allocation becomes optimal. A different conclusion is drawn for the case where both M and N grow large together, in which case improved rates are achieved as M grows with fixed M/N ratio, and the relative gains over the equal power allocation diminish as M/N grows. Moreover, we also provide applicable values of M/N under an acceptable power allocation gain threshold, which can be used to determine when the proposed power allocation schemes yield appreciable gains and when they do not. From the network point of view, the proposed scheduling approach can achieve almost the same performance as the joint power allocation after one scheduling round, with much reduced complexity.

In this paper, we investigate the uplink rate of multicell massive multiple-input multiple-output (MIMO) systems. We assume the channel is estimated through uplink training with MMSE estimation. Unlike previous studies, the channel between users and the base station (BS) in the same cell is modeled to be Ricean fading, in which the fast fading matrix is assumed to have a deterministic component as well as a Rayleigh-distributed random component, and the Ricean K-factor of each user is supposed to be different. The effect of pilot contamination is analyzed and we derive a closed-form approximation for the achievable uplink rate that holds for any finite number of BS antennas (M). Based on it, we find that increasing the proportion of line-of-sight (LOS) component can improve the uplink performance. In particular, with both very large M and Ricean K-factor, the uplink rate grows infinite, which means that the pilot contamination can be eliminated completely. However, the increase of users' transmit power will make the uplink rate approach a constant value even with unlimited M. In addition, we show that with no reduction in the rate performance, each user's power can be most scaled down to 1/√M with Rayleigh fading channel and to 1/M with non-zero Ricean K-factor.

In this paper, we study the achievable uplink rates of massive multiple-input multiple-output (MIMO) systems using maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming imperfect channel state information (CSI). Unlike all previous studies, the fast fading MIMO channel matrix here is modeled to have an arbitrary-rank deterministic component as well as a Rayleigh-distributed random component. In particular, it is found that with a non-zero Ricean K-factor, the approximations and the exact uplink rates converge to the same constant value if the number of base station antennas, M, grows large, while the transmit power of each user is scaled down proportionally to 1/M. However, if the channel is Rayleigh fading, we can only cut the transmit power of each user proportionally to 1/√M. In addition, we show that with increasing Ricean K-factor, the uplink rates will converge to fixed values for both MRC and ZF receivers.