We consider fairness scheduling in a user-centric cell-free massive MIMO network, where L remote radio units, each with M antennas, serve $K \approx LM$ user equipments (UEs). Recent results show that the maximum network sum throughput is achieved where ${K_{{\text{act}}}} \approx \frac{{LM}}{2}$ UEs are simultaneously active in any given time-frequency slots. However, the number of users K in the network is usually much larger. This requires that users are scheduled over the time-frequency resource and achieve a certain throughput rate as an average over the slots. We impose throughput fairness among UEs with a scheduling approach aiming to maximize a concave component-wise non-decreasing network utility function of the per-user throughput rates. In cell-free user-centric networks, the pilot and cluster assignment is usually done for a given set of active users. Combined with fairness scheduling, this requires pilot and cluster reassignment at each scheduling slot, involving an enormous overhead of control signaling exchange between network entities. We propose a fixed pilot and cluster assignment scheme (independent of the scheduling decisions), which outperforms the baseline method in terms of UE throughput, while requiring much less control information exchange between network entities.

The pilot contamination in cell-free massive multiple-input-multiple-output (CF-mMIMO) must be addressed for accommodating a large number of users. In previous works, we have investigated a decontamination method called subspace projection (SP). The SP separates interference from co-pilot users by using the orthogonality of the principal components of the users’ channel subspaces. For CF-mMIMO system with SP, non-overloaded pilot assignment (PA) and overloaded PA can be considered. Non-overloaded PA, where each radio unit (RU) does not assign the same pilot to different users, limits the number of associated RUs per each UE and this reduces the potential spectral efficiency (SE) of the system. On the other hand, non-overloaded PA reduces channel estimation error induced by contamination. This paper compares non-overloaded PA and overloaded PA, and introduces overloaded PA methods adjusted for the decontamination in order to improve the sum SE of CF systems. Numerical simulations show that the overloaded PA methods give higher SE than that of non-overloaded PA at a high user density scenario.

We consider a cell-free wireless system operated in Time Division Duplex (TDD) mode with user-centric clusters of remote radio units (RUs). Since the uplink pilot dimensions per channel coherence slot is limited, co-pilot users might incur mutual pilot contamination. In the current literature, it is assumed that the long-term statistical knowledge of all user channels is available. This enables Minimum Mean-Square Error channel estimation or simplified dominant subspace projection, which achieves significant pilot decontamination under certain assumptions on the channel covariance matrices. However, estimating the channel covariance matrix or even just its dominant subspace at all RUs forming a user cluster is not an easy task. In fact, if not properly designed, a piloting scheme for such long-term statistics estimation will also be subject to the contamination problem. In this paper, we propose a new channel subspace estimation scheme explicitly designed for cell-free wireless networks. Our scheme is based on 1) a sounding reference signal (SRS) using latin squares wideband frequency hopping, and 2) a subspace estimation method based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme ensures that for any user and any RU participating in its cluster, only a few pilot measurements will contain strong co-pilot interference. These few heavily contaminated measurements are (implicitly) eliminated by R-PCA, which is designed to regularize the estimation and discount the “outlier” measurements. Our simulation results show that the proposed scheme achieves almost perfect subspace knowledge, which in turns yields system performance very close to that with ideal channel state information, thus essentially solving the problem of pilot contamination in cell-free user-centric TDD wireless networks.

This paper proposes a clustering scheme suitable for a centralized millimeter wave Cell-Free massive MIMO with hybrid beamforming (BF). Conventionally AP-wise clustering schemes have been proposed for a decentralized architecture, that suppresses multi-user interference at each access point (AP) with local digital precoding and analog BF of each radio frequency (RF) chain, in order to obtain better performance efficiently. However in the centralized architecture, designing the precoder and cluster at central processing unit (CPU) with considering all RF chains of all APs together, the AP-wise clustering could not fit well especially when a large number of UEs are multiplexed. Because the RF chains with high coupling loss (the sum of the path loss and BF gain of the analog beam) could be included in the APs of the cluster to each UE. The proposed method forms a cluster on a per-RF-chain basis, and can select RF chains with lower coupling loss for each UE at the CPU regardless of the AP by incorporating the analog BF gain of each RF chain to form a cluster for each UE. Through simulation evaluations, we show that the hybrid BF with the proposed clustering scheme can obtain the superior spectral efficiency while effectively reducing the complexity of the centralized digital precoding.

Cell-free massive MIMO is a variant of multiuser MIMO and massive MIMO, in which the total number of antennas LM is distributed among the L remote radio units (RUs) in the system, enabling macrodiversity and joint processing. Due to pilot contamination and system scalability, each RU can only serve a limited number of users. Obtaining the optimal number of users simultaneously served on one resource block (RB) by the L RUs regarding the sum spectral efficiency (SE) is not a simple challenge though, as many of the system parameters are intertwined. For example, the dimension $\tau_{p}$ of orthogonal Demodulation Reference Signal (DMRS) pilots limits the number of users that an RU can serve. Thus, depending on $\tau_{p}$, the optimal user load yielding the maximum sum SE will vary. Another key parameter is the users’ uplink transmit power $P_{\mathrm{tx}}^{\mathrm{ue}}$, where a trade-off between users in outage, interference and energy inefficiency exists. We study the effect of multiple parameters in cell-free massive MIMO on the sum SE and user outage, as well as the performance of different levels of RU antenna distribution. We provide extensive numerical investigations to illuminate the behavior of the system SE with respect to the various parameters, including the effect of the system load, i.e., the number of active users to be served on any RB. The results show that in general a system with many RUs and few RU antennas yields the largest sum SE, where the benefits of distributed antennas reduce in very dense networks.

This paper proposes hybrid signal processing schemes for the uplink cell-free massive MIMO; these schemes serve to reduce fronthaul loads to obtain a scalable centralized processing architecture. In this architecture, received signals of multiple receive antennas at the access points (APs) are compressed into fewer streams by local spatial signal processing and then the streams are forwarded to a central processing unit (CPU) via fronthaul, and the CPU performs scalable processing for channel estimation and signal detection based on partial minimum mean squared error (PMMSE). We propose two kinds of concrete local signal processing methods for this hybrid processing architecture: one is based on MMSE, and the other is based on principal component analysis (PCA) with eigenvalue decomposition (EVD). For the EVD, a local vector selection based EVD (LVS-EVD) that selects uniform number of eigenvectors for each AP in a standalone way, and a global vector selection based EVD (GVS-EVD) that determines the dimensions of the weight vector of each AP in the CPU, are further considered. Computer simulations verify the approaches and compare their effectiveness. In addition, we show that the GVS-EVD scheme can be operated with significantly reduced fronthaul loads without severe performance degradation.

Measurements of the propagation channels in real-world environments form the basis of all realistic system performance evaluations, as foundation of statistical channel models or to verify ray tracing. This is also true for the analysis of cell-free massive multi-input multi-output (CF-mMIMO) systems. However, such experimental data are difficult to obtain, due to the complexity and expense of deploying tens or hundreds of channel sounder nodes across the wide area a CF-mMIMO system is expected to cover, especially when different configurations and number of antennas are to be explored. In this paper, we provide a novel method to obtain channel data for CF-mMIMO systems using a channel sounder based on a drone, also known as a small unmanned aerial vehicle (UAV). Such a method is efficient, flexible, simple, and low-cost, capturing channel data from thousands of different access point (AP) locations within minutes. In addition, we provide sample 3.5 GHz measurement results analyzing deployment strategies for APs and make the data open source, so they may be used for various other studies. To our knowledge, our data are the first large-scale, real-world CF-mMIMO channel data.

We consider a scalable user-centric wireless network with dynamic cluster formation as defined by Björnsson and Sanguinetti. After having shown the importance of dominant channel subspace information for uplink (UL) pilot decontamination and having examined different UL combining schemes in our previous work, here we investigate precoding strategies for the downlink (DL). Distributed scalable DL precoding and power allocation methods are evaluated for different antenna distributions, user densities and UL pilot dimensions. We compare distributed power allocation methods to a scheme based on a particular form of UL-DL duality which is computable by a central processor based on the available partial channel state information. The new duality method achieves almost symmetric "optimistic ergodic rates" for UL and DL while saving considerable computational complexity since the UL combining vectors are reused as DL precoders.

The bit reliability based (BRB) and weighted bit reliability based (wBRB) algorithms are non-binary low-density parity-check (LDPC) code decoding algorithms with an excellent tradeoff between computational complexity and performance. However, the performance of these algorithms needs further improvement. We apply deep learning to these algorithms. Weights are assigned to each edge of the Tanner graphs of the non-binary LDPC codes in the proposed algorithms. We demonstrate the effectiveness of applying deep learning to the BRB and wBRB algorithms in terms of implementation and performance. The proposed algorithms achieve an approximately 0.3 dB higher bit error rate performance than the original algorithms in the high SNR region. The increase in computational complexity and memory consumption does not significantly change the implementation of the algorithms.

In recent years, it has become common for vehicles to connect to networks via radio access networks to exchange various data. In the future, the number of service providers that provide information to and collect information from vehicles will increase, and the traffic from these service providers may occupy a large portion of the radio resources consumed in the radio access network. Therefore, it is desirable to improve the frequency utilization efficiency of the traffic related to these service providers. In this paper, we provide a quantitative demonstration of the concept of traffic control using the load information on radio access networks that the mobile network operator can collect from its own network, the information on radio environment that the service provider collects, and the route planning of the vehicle to its destination. Specifically, based on the above information, traffic control will be performed within the range where the vehicle can move within the allowable delay time, such as downloading data at points with low load and good radio environment in the downlink, and controlling the streaming rate according to the load and radio environment in the uplink. Computer simulations show that for downlink, both the frequency utilization efficiency and UE throughput during download can be improved, and for uplink, the frequency utilization efficiency can be improved while improving the amount of successfully transmitted data.

Cell-free massive multiple-input multiple-output systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G. Recently, a measurement-based evaluation revealed that the performance of a semi-distributed deployment, where each AP has multiple antennas, is comparable to that of a fully-distributed deployment in terms of coverage in an indoor environment while reducing the number of APs. In this paper, we analyze the performance of various antenna distribution configurations, and show that semi-distributed deployments outperform fully-distributed deployment remarkably from both spectral and energy efficiency points of view. These characteristics of semi-distributed deployments enable us to construct more cost-effective networks, which is an important indicator to deploy the systems in real environment.

Research and development for the commercial deployment of 5G are accelerating and millimeter wave bands, which can utilize a wider bandwidth to realize larger capacity than conventional frequency bands, have become a focal point. These millimeter wave bands are different from conventional frequency bands in propagation characteristics, such as large blockage loss. Furthermore, the 5G system also assumes the case of new indoor use, such as industrial robots in factories and so forth. Therefore, a suitable propagation model for millimeter wave bands is necessary to evaluate 5G system performance by simulation or to design 5G area. In this paper, measurement campaigns of the path loss, the power delay profile and the azimuth angular profile of arrival were conducted in an indoor factory environment. In addition, we propose a suitable propagation estimation model for the indoor environment and we clarify that the proposed model is more suitable than the conventional model of the International Telecommunication Union Radiocommunication sector (ITU-R) in a subway platform by applying the measured results to a subway platform.

In this paper, we propose a virtual full-duplex cooperative non-orthogonal multiple access (NOMA) framework for a downlink two-hop network assisted by multiple half-duplex decode-and-forward (DF) relay stations (RSs). In the proposed virtual full-duplex framework, the RSs except for a selected RS to forward the received signal suffer from inter-RS interference since both the BS and the selected RS transmit the super-imposed signal simultaneously. To address this problem, we propose an RS selection algorithm with adaptive inter-RS interference management and we mathematically attain the closed-form outage probability, which is challenging in general. In addition, we investigate diversity-multiplexing tradeoff (DMT) performance of a modified version of the proposed RS selection algorithm based on a discrete Markov chain, which adaptively resets the successive transmission. Simulation results show that the proposed RS selection algorithm outperforms the conventional cooperative NOMA algorithm in terms of both the outage probability and the DMT, which has the best performance reported in the literature.

In this paper, an explicit interpretation on how different enabling technologies over generations are brought up and coordinated for migration from a distributed, to a centralized, and then to a virtualized RAN for 5G and beyond cellular is given. In doing so, by pointing out major enabling technologies, we present a framework for RAN evolution and discuss its architectural migration over time and generations, namely from the current 4G to the upcoming 5G and beyond mobile networks, based on both functional split and physical placement in access networks.

In this paper, we propose a novel virtual full-duplex cooperative non-orthogonal multiple access (NOMA) technique for a downlink two-hop cellular network which consists of a single base station (BS), two mobile stations (MSs), and K half-duplex decode-and-forward (DF) relay stations (RSs). In the proposed technique, the BS sends super-imposed signals for two MSs via the RSs in each transmission phase, while a selected RS via two-stage relay selection sends the signals to two MSs. Thus, the multiplexing loss due to half-duplex operation of RSs can be overcome by allowing for both the BS and a selected RS to send data at the same time. In the proposed cooperative NOMA, we adaptively reset the successive transmission according to decoding status at RSs. As main results, we mathematically analyze outage probability and diversity-multiplexing tradeoff (DMT) of the proposed technique. Extensive computer simulations show that the proposed technique significantly outperforms the existing schemes in terms of both outage probability and DMT.

This paper describes study results regarding a feature detection scheme using a Cyclic Prefix (CP) that is appended to an OFDM signal. The detection scheme is especially important when used as a sensing technology in advanced systems such as Device-to-Device communication. Herein, several performance characteristics of the signal processing involved in the feature detection are described with an analytical expression and examined with software simulation. Then, supposing that two OFDM systems using different CP lengths are operated in the same frequency band, some applications are addressed such as an estimation method for the Input C/N (Carrier-to-Noise power ratio), a system discrimination scheme and a timing offset detection method.

In this paper, we discuss the efficient use of a mobile router with multiple radio access technologies (RATs), called a cognitive mobile router (C-MR). In a heterogeneous wireless network, where various infrastructure-based RATs have already been deployed, C-MR selects a high quality RAT from among those available and provides a high quality mobile communication to connecting Wi-Fi user terminals. However, considering the significant growth in mobile data traffic, a huge amount of mobile data passes through C-MR and critical packet losses may occur due to its limited computational resources. Moreover, if the quality of the selected RAT is insufficient, incoming packets are stacked in C-MR and this leads to performance degradation due to packet loss, timeout and so forth. In this paper, we propose two novel schemes to avoid such redundant performance degradation, one is a novel packet queuing scheme that operates such a huge amount of mobile data traffic properly even with the limited computational resources. The other is an energy-efficient and accurate RAT quality selection method that avoids miss-selection of a suitable RAT. Through computer simulations, we have confirmed that C-MR deployment can realize more 40% packets delivery with higher priority compared with the no C-MR situation even while maintaining lower infrastructure load and lower power consumption of user terminals.

Wi-Fi is one of the solutions to overcome the explosion of mobile data traffic and improvement of Wi-Fi performance intensifies the cellular data offload to Wi-Fi. Wi-Fi AP (Access Point) selection at the STA (STAtion) is a key function to improve Wi-Fi throughput. Our proposed method is practical for the AP selection. If an STA can know the throughputs between the STA and surrounding APs before exchanging Wi-Fi packets with an AP, the STA can select the optimum AP that promises high throughput. In this paper, we focus on a throughput estimation method that is applicable to AP selection, and discuss the accuracy of the estimated throughput. We propose a novel Wi-Fi throughput estimation method with passive measurement and without modification for the Wi-Fi standard. The proposed method consists of three stages: measurement of the time ratio used by each Wi-Fi device, selection of dominant Wi-Fi devices and calculation of estimated throughout. Our proposed method considers the influence of CSMA/CA that affects Wi-Fi throughput that changes based on the number of Wi-Fi devices sharing the same frequency band and the traffic load of each device. We evaluate the effectiveness of the proposed method by experiments (estimated before communication) and compare the estimated throughput and the actual throughput (measured during communication) in certain environments. The proposed method is effective for estimating Wi-Fi throughput. The RMSE (Root Mean Square Error) of the proposed method decreases to 29~33% that of conventional methods. Estimated throughput with our proposed method is always higher than measured throughput. Therefore, modification of the proposed method to decrease the difference between the estimated and measured value is a subject for future study. As some systems influence Wi-Fi, considering these influences in a throughput estimation process is also recommended.

To utilize wireless sensor networks (WSN) effectively, maximization of network lifetime and minimization of response time to data queries are very important issues. To enhance the functionality and availability of conventional WSNs, we propose the new concept of a Multi-Mode Wireless Sensor Network (MM-WSN) in which nodes with only limited battery power are equipped with different types of radio access technologies. Nodes in a MM-WSN can use both single-hop and multi-hop delivery of data and select the most suitable option according to the situation. In this paper, we show that our proposed method provides a radio access selection method that achieves energy-efficient and high-speed data reports. The effectiveness of our proposal is shown by means of computer simulations.

This paper proposes a new way of measuring signal to interference and noise ratio (SINR) at a low level of power consumption for vertical handover. In order to select the most suitable radio access networks (RAN) in vertical handover, the SINR of the alternative RAN should be measured at a certain interval while communicating with the existing RAN. In our proposal, the SINR measurement interval for the alternative RAN is controlled on the basis of SINR fluctuations in order to maintain high tracking ability and reduce power consumption during monitoring operations for vertical handover. In addition, a simple probability inequality is applied to detect SINR fluctuations with high precision and achieve low computational complexity. The effectiveness of the proposed monitoring method was verified through computer simulations and the results showed that the averaged SINR could be measured to an accuracy of about 1 dB while maintaining sleep mode at about 30%.

This paper presents an energy efficient radio access network (RAN) selection for vertical handover between heterogeneous networks. The proposed RAN selection switches evaluation bases by application and adaptively selects a RAN with low energy consumption. In addition, the selection employs a penalty function that avoids discarded vertical handovers to reduce handover overhead and network loading by reducing the integrations.

In a pico-cell architecture, co-channel interference between adjacent cells can cause serious problems. With a centralized control approach based on interference information reported by each subscriber station (SS), we propose a novel channel allocation algorithm applying the graph theory. This method directly determines the optimal channel allocation and minimizes inter-cell co-channel interference. Both computer simulations and experiments based on the implementation demonstrate that our proposed algorithm is effective.

In pico-cell architecture, although utilizing a simple base station and a simplified allocation design help to reduce bit cost, frequency channel interference between/among adjacent cells becomes a serious problem. The pico-cell system is supposed to be used mainly for packet-based communication and the carrier to interference power ratio (C/I) degradation leads to poor network performance. To overcome this drawback, we propose a novel channel allocation algorithm, i.e., a centralized approach that works based on interference information reported by each subscriber station (SS). Both computer simulations and experiments based on implementation clarify that our proposed algorithm is effective

Ad hoc networks have recently become a hot topic. In ad hoc networks, battery power is an important resource, since most terminals are battery powered. Terminals consume extra energy when their network interfaces are in the idle state or when they overhear packets not destined for them. They should, therefore, switch off their radio when they do not have to send or receive packets. IEEE802.11 features a power saving mechanism (PSM) in the distributed coordination function (DCF). In PSM for DCF, nodes must stay awake for a fixed time, called the ATIM window (ad-hoc traffic indication map window). If nodes do not have data to send or receive, they enter the doze state except for during the ATIM window. However, ad hoc networks with PSM have larger end-to-end delays to deliver packets and suffer lower throughput than the standard IEEE802.11. To solve this problem, this paper proposes a protocol that reduces delay and achieves high throughput and energy efficiency. Simulation results show that our proposal outperforms other PSMs in terms of throughput, end-to-end delay and energy efficiency.