Cell-free massive multiple-input-multiple-output (CF-mMIMO) is regarded as one of the promising technologies for next-generation wireless networks. However, due to its distributed architecture, geographically separated access points (APs) jointly serve a large number of user-equipments (UEs), and there are inevitably discrepancies in the arrival time of transmitted signals. In this paper, we investigate millimeter-wave (mmWave) scalable CF-mMIMO orthogonal frequency division multiplexing (OFDM) systems with asynchronous reception in a wide area coverage scenario, where asynchronous timing offsets may exceed far beyond the cyclic prefix (CP) duration. To address the issue, we propose a novel per-beam timing advance (PBTA) hybrid precoding architecture and derive closed-form expressions of the spectral efficiency (SE) for downlink asynchronous transmission. Both scalable centralized and distributed implementations are taken into account. Furthermore, we formulate the sum rate maximization problem and develop two low-complexity joint beam selection and UE association (BSUA) algorithms considering the impact of asynchronous timing offset. Simulation results demonstrate that asynchronous interference can severely degrade performance in wide-area scenarios, and our proposed PBTA scheme exploits beam-domain synchronization to align signal arrivals, effectively suppressing asynchronous interference and delivering notable performance gains. Additionally, the proposed BSUA algorithms achieve superior SE performance with low computational complexity.

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising technology for sixth-generation mobile communication systems. Building upon conventional CF-mMIMO, the cell-free radio access network (CF-RAN) architecture distributes physical-layer functionalities among access points (APs), edge distributed units (EDUs), and user-centric distributed units (UCDUs), striking a balance between complexity and performance. However, prior studies on CF-RANs have primarily focused on the physical-layer, while the scalability of the medium access control (MAC) layer remains insufficiently explored. This paper proposes a novel hierarchical scalable CF-RAN architecture that fully exploits the functional potential of distributed UCDUs, enabling a comprehensive decentralized paradigm spanning from the physical-layer to the MAC-layer. Closed-form uplink spectral efficiency (SE) expressions are derived for maximum ratio (MR), distributed full-pilot zero-forcing (FZF), and distributed joint partial zero-forcing (JP-ZF) combining, explicitly accounting for imperfect channel state information and pilot contamination. The analytical insights reveal the improved scalability and how distributed processing and partial information availability affect system performance. To support large-scale deployments, we further develop a structured massive access scheme, including UCDU-EDU deployment, UE-UCDU association, distributed pilot assignment and AP-UE association, centralized refinement, and distributed power control. Simulation results verify the accuracy of the theoretical analysis and demonstrate the superior SE, favorable fairness, and enhanced scalability of the proposed schemes.

As a critical component of beyond fifth-generation (B5G) and sixth-generation (6G) mobile communication systems, ultra-reliable and low-latency communication (uRLLC) imposes stringent requirements on both latency and reliability. In recent years, with the evolution of mobile communication networks, centralized and distributed processing schemes for cell-free massive multiple-input multiple-output (CF-mMIMO) have attracted significant attention. This paper investigates the performance of a novel scalable cell-free radio access network (CF-RAN) architecture featuring multiple edge distributed units (EDUs) under the finite block length regime. Closed-form expressions for the upper and lower bounds of the expected sum spectral efficiency (SE) are derived, where centralized and fully distributed deployment can be treated as two special cases, respectively. Furthermore, the spatial distributions of user equipment (UE) and remote radio units (RRUs) are analyzed, revealing that interleaving RRUs deployment associated with the EDUs can enhance SE performance under finite block length constraints with a specified transmission error probability. This paper also compares Monte-Carlo simulation results with multi-RRU clustering-based collaborative processing, validating the accuracy of the space-time exchange theory in the scalable CF-RAN scenario. By deploying scalable EDUs, a practical tradeoff between latency and reliability can be achieved through the spatial degree-of-freedom (DoF), thereby offering a distributed and scalable realization of the space-time exchange theory.

In the previous generations of mobile communication system, mobility management is based on downlink measurement. In other words, reference signals in downlink are measured at the user equipment (UE) and the reference signal receiving quality (RSRQ) is reported back to the base station (BS). The power consumption and signaling overhead at the UE are significant, especially in the 5G mobile network systems where smaller and denser cells are deployed. To solve this problem, a novel mobility management scheme based on uplink reference signal is proposed in this paper. Unlike legacy mobility schemes, the proposed scheme allows the UE to transmit an uplink reference signal and BSs at surrounding cells to perform measurement in uplink. By taking advantage of the uplink measurement, the measurement report is eliminated and the handover delay is also shortened. Moreover, the power consumed for downlink measurement is also saved. Thus, the signaling overhead, paging miss rate and UE power consumption for uplink based mobility are lower than those for traditional downlink based scheme in LTE systems. Simulation results shows that compared with downlink based mobility, the paging miss rate and UE power consumption can be reduced by up to 38% and 63%, respectively.

In this paper, a joint linear pre-coder and combiner optimization scheme is proposed to reduce the multi- user interference in the distributed antenna system (DAS). The pre-coder is designed based on the modified weighted signal-to-caused interference ratio (SCIR) which considers the interference on others caused by the desired user and the users channel condition. And the combiner is chosen based on the signal-to- interference-plus-noise ratio (SINR) which considers the interference on the desired user caused by others. The average user rate and symbol error rate (SER) are investigated through Monte Carlo simulation. The simulation results show that the proposed scheme outperforms other schemes.

The massive multiple-input multiple-output (MIMO) has been proposed as one of the key technologies for the next generation wireless communication systems. Uniform planar array (UPA) antenna structures have been considered in many researches because it is able to deploy a large number of antennas in a relatively small area. There are lots of performance superiorities in massive MIMO systems. However, in practice, these superiorities is achieved only when the uplink and downlink channel state information (CSI) are known perfectly by both the base station (BS) and users. In frequency division duplex (FDD) massive MIMO systems, a large percentage of precious downlink capacity is reserved by training sequence for channel estimation due to the large number of antennas. In this paper, a design method of optimal training sequence is studied based on the downlink closed-loop training scheme with UPA, in which the Kalman filter is used for CSI acquisition. Simulation results show that the proposed optimal training sequence based on the closed-loop training scheme outperforms the existing ones.

It is of interest for future mobile communication systems that mobile relay nodes (MRNs), i.e., relays mounted on top of transportation vehicles, are deployed in order to improve the system performance. A multi-MRN aided multiuser system is considered in this paper. The performance of the direct transmission mode and the MRN assisted transmission mode under different vehicle penetration loss (VPL) are compared. The users are separated into two categories in which the direct transmission mode and the MRN assisted transmission mode are selected to employ, respectively. A novel power allocation algorithm is proposed to improve the average system capacity under the constraint of total transmit power. The different power allocation schemes are applied for users in two categories to improve the system performance. It is demonstrated by simulation results that the proposed algorithm outperforms the average power allocation algorithm.

In the massive Multiple-Input Multiple-Output (MIMO) system, a large number of transmit antennas are deployed at base stations. The massive MIMO has been proposed as one of the key technologies for the next generation of wireless communication systems. A major problem in practice to realize the capacity improvement promised by the massive MIMO technology is the availability of the channel state information in downlink at the base station. In frequency division duplexing (FDD) massive MIMO systems, the overhead of the training sequences could overwhelm the precious downlink resources due to the large number of transmit antennas. Therefore, a training sequences design approach that exploits the channel spatial correlation is proposed to reduce the overhead of training sequences. The optimal training sequence structure is analyzed and the length of optimal training sequence which could maximize the achievable data rate is evaluated by the Monte Carlo simulations in this paper. It is shown that the overhead is significantly reduced and a trade-off between accuracy of channel estimation and the system achievable data rate is achieved simultaneously by the proposed training sequences design method.

In this paper, coordinated beamforming scheme in distributed antenna systems (DASs) used for energy efficient transmission is developed. The major problem is non-convex of the optimization problem and is difficult to solve directly due to the fractional form. It is transformed into an equivalent subtractive-form optimization problem, by which we tackle this problem through a two-layer optimization algorithm. The out layer problem is solved by one dimension search method. The inner-layer sub-problem is solved with an optimal beamforming algorithm which is developed by exploiting the relationship between the user rate and the mean square error (MSE) of the received signal. The simulations illustrate that the higher energy efficiency can be achieved with the proposed algorithm than the weighted minimum mean square error (WMMSE) algorithm.

With the rapid growth of global mobile data transmission, the conventional multiple-input multiple-output (MIMO) systems are not able to accommodate the increasing data demand. The massive MIMO has been proposed as one of the key technologies for the next generation wireless communication systems. Compared to the conventional MIMO systems, massive MIMO systems are advantaged in energy and spectral efficiency. However, the advantage of massive MIMO systems is realized only when the channel state information (CSI) is acquired by both the base station (BS) and users accurately. In frequency division duplex (FDD) massive MIMO systems, the overhead of channel estimation could overwhelm the precious downlink resource. In this paper, a design method of optimal training sequence is studied based on the sequential channel estimation scheme with Kalman filter. Simulation results show the performance of the proposed optimal training sequence outperforms the exist ones.

In this paper, we study the large-scale multi-user distributed antenna system (DAS) for hot-spot coverage in the future 5G wireless network. Firstly, the multi- user system model of large- scale DAS is introduced and a simple random pilot reuse scheme is presented to reduce the overhead of pilot. Then, the sum-rate of system is derived, and its asymptotical performance is studied when the number of antennas goes to infinity. Moreover, an attractive method for large-scale DAS using matrix sparsification is presented to simplify the signal processing, which is of great significance for the future research. Finally, we study the detection bit error ratio (BER) and area spectral efficiency of large-scale DAS.

Networking coding is investigated to improve the throughput of wireless networks without additional resources used. Most of the previous research work focus on the AWGN channel. However, the radio signal is subject to severe fading over wireless channel. In this paper, the network coding outage capacity of two-way system under Rayleigh fading channel is studied by considering all the transmission stages. The system model is established through deriving the outage probability. In order to obtain the maximal outage capacity, the feasible region searching is used to solve this optimization problem. It is shown that the outage capacity is improved significantly by network coding through the numerical results. Regardless of various channel parameters, the best time allocated to network coding stage occupies about one third of the total period. The maximal outage capacity would be achieved through allocating the transmission data rate and time of each stage appropriately.

The throughput of wireless network can be improved by wireless network coding, without adding additional resources. However, most of the previous work focuses on the AWGN channel. In fact, the signal is subject to severe fading over wireless channel. The network coding reliable throughput of two-way system under Rayleigh fading channel is studied in this paper, by considering all the transmission stages. First, the system model is established through studying the outage probability. To obtain the maximal throughput, feasible region searching is used to achieve this optimization problem. It is shown in the numerical results that network coding significantly improves the system throughput. The best time allocated to network coding stage is about one third of the total time, regardless of various channel parameters. Through allocating the sending data rate and time appropriately, the maximal system throughput would be achieved.

The problem of resource allocation for OFDMA downlink is investigated in this paper. The problem is formulated as a rate adaptive problem, which is to maximize the system throughput while subject to total transmission power constraints. A distributed resource allocation scheme to solve this combinatorial optimization problem is presented. The problem is decomposed into a principal problem with regard to the base station and several subproblems with regard to the users. In particular, we consider regarding users as relay nodes that could send feedback information back to the base station. By solving the subproblems, each user obtains its own subcarriers and power allocation scheme. The base station could make sure all the constraints are met and the system throughput is maximized by solving the principal problem. Simulation results show that the proposed algorithm converges quickly to an approximated optimal solution in a small number of iterations.

In this paper, frequency-domain one-tap weight control for single-carrier multiple access with multiple antennas is considered. Adaptive antenna array (AAA) weight control, diversity combining (DC) weight control and least square (LS) weight control are studied. These three weight control algorithms belong to multiple-input multiple-output (MIMO) beam forming, MIMO diversity and MIMO multiplexing, respectively. The weight control algorithms will be evaluated and compared in terms of computational complexity as well as bit error rate (BER) performance in a frequency-selective Rayleigh fading channel.

Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and downlink connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types.

Distributed antenna system (DAS) can improve the spectral efficiency of the cellular system, especially at the cell edge. In the paper, we consider the cognitive radio technique to further improve the cell edge performance of the DAS. We consider the following scenario: multiple secondary users (SUs) share the same spectrum with the primary user (PU) at the cell edge, and one or two SUs are selected to forward signals for the PU. First, we study the transmit scheme using MRT, and the downlink capacity of the PU is analyzed. Then, we generalize the idea of dynamic relay selection by allowing one or two SUs to cooperate. The dynamic selection scheme can be realized based on the optimal signal noise ratio (SNR). Simulation results show that dynamic selection of one relay can achieve better performance than the static selection of one, and dynamically selecting one SU outperforms the dynamic selection of two because of the increasing of the resource consumption along with the number of relay nodes. With different choices of the channel state information (CSI), the large-scale fading achieves a better tradeoff between the performance gain and processing complexity. The effect of different selection threshold on the average capacity is also given.

In this paper, we consider a case where two distributed MIMO systems share the same spectrum. Each of them has to dynamically detect the spectrum of the other. The system model is established and the joint spectrum sensing scheme is studied. The optimal weighting factors are derived. Closed-form expressions of the probability of false alarm and sensing are provided. Simulation results show that the proposed joint sensing scheme can achieve much better sensing performance than that of the single-sensor scheme.

Cooperative sensing based on energy detection in distributed antenna systems is considered in this letter. We investigate soft combination of the observed energies from different cognitive radio users. Based on the Neyman-Pearson criterion, an optimal soft combination scheme which can maximize the detection probability for the constant false alarm rate is obtained. Closed-form expressions for the detection probability and false alarm rate for the optimal soft combination scheme are derived. Finally, through numerical results the improvement in cooperative sensing schemes with the optimal soft combination is shown.

In this letter, we consider a case in which only one radio frequency (RF) signal is of interest over multiple RF signals with contiguous spectrums (i.e., these RF signals are adjacent to each other). The minimum valid bandpass sampling range for a desired RF signal over multiple ones is derived under the assumption of contiguous spectrums. The sampling frequency confined by the derived minimum valid sampling range is significantly reduced compared to the case in which all the RF signals are desired. The minimum valid bandpass sampling frequency can be further reduced when the desired RF signal has symmetric double sidebands.

In this paper, we present a generalized multi-carrier (GMC) transmission technique in multi-input and multi-output (MIMO) channel environments for beyond 3G mobile communications. Several key techniques including the design and implementation of the GMC synthesis/analysis system, adaptive double cyclic timeslot structure, pilot design and channel estimation, and iterative detection/decoding are briefly discussed. Link-level simulation results in Rayleigh fading MIMO channels are provided