This paper proposes a low-complexity digital post-distortion (DPoD) technique using a frequency-domain attention-based neural network (FDA-NN) for compensating nonlinear distortions in power amplifiers supporting extremely wideband signals in the terahertz (THz) band. The proposed method efficiently captures the frequency-dependent memory effects of the power amplifier by applying a discrete Fourier transform (DFT) to the input signal and generating an attention signal that weights the frequency components according to their relevance for nonlinear distortion compensation. The performance of the proposed method is evaluated through experiments using a wideband power amplifier operating in the THz band and an orthogonal frequency-division multiplexing (OFDM) signal with a bandwidth of 4.8 GHz. The results demonstrate that the FDA-NN achieves comparable performance to more complex models, such as deep neural networks (DNN) and long short-term memory (LSTM) networks, while reducing the computational complexity by 48.9% and 78.7% in terms of floating-point operations (FLOPs), respectively. The proposed FDA-NN presents a promising solution for efficient nonlinear distortion compensation in future extremely wideband THz communication systems.

We consider a user-centric cell-free massive MIMO wireless network withLremote radio units, each withMantennas, servingKsingle-antenna user devices (UEs). Most of the current literature considers the regimeLM≫K, where theKUEs are active on each time-frequency slot, and evaluates the system performance in terms ofergodic rates. In this paper, we take a quite different viewpoint. We observe that the regime ofLM≫Kcorresponds to a lightly loaded system with low sum spectral efficiency (SE). In contrast, in most relevant scenarios, the number of UEs is much larger than the total number of antennas (think of a sport event withK~ 10, 000 users andML~ 200 antennas). To achieve high sum SE and handleK≫ML, users must be scheduled over the time-frequency resource. The number of active usersKact⩽Kmust be carefully chosen such that: 1) the network operates close to its maximum SE; 2) the active user set must be chosen dynamically over time in order to enforce fairness in terms of per-user time-averagedthroughput rates. The fairness scheduling problem is canonically formulated as the maximization of a suitable concave componentwise non-decreasingnetwork utility functionof the per-user rates. The intermitted user transmission due to scheduling imposes slot-by-slot coding/decoding, which in turn prevents the achievability of ergodic rates. Hence, we model the per-slot service rates using information outage probability. In order to obtain a tractable problem, we make a “decoupling” assumption on the CDF of the instantaneous mutual information seen at each UEkreceiver. We approximately enforce this condition by introducing a conflict graph that prevents the simultaneous scheduling of users with large pilot contamination conflict and propose an adaptive scheme for instantaneous service rate scheduling based on locally estimating the mutual information CDF at each UE. Overall, the proposed dynamic scheduling is the first to address such system dimensions with tens of thousand users in a scalable way, is robust to system model uncertainties, and can be easily implemented in practice.

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.

In this paper, we propose low-complexity digital predistortion (DPD) schemes based on FastGRNN to compensate for the nonlinearity of RF power amplifiers. Conventionally, high-precision recurrent neural network (RNN) models, such as long short-term memory (LSTM) and gated recurrent unit (GRU), have been used to model the behavior of amplifiers, and their excellent compensation performance has been shown in terms of error vector magnitude (EVM) and adjacent channel power ratio (ACPR) has been demonstrated. However, their complex structures result in high computational complexity. To solve this issue, the proposed method is designed to significantly reduce the complexity without significant performance degradation by appropriately applying the FastGRNN models to the DPD. Complexity analysis and experiments using a power amplifier in the 2.0 GHz frequency band showed that the proposed method achieved comparable EVM performance to LSTM with 29.2% floating point operations (FLOPs) and 27.1% trainable parameters.

In Japan, the 5th generation mobile communication system (5G) became commercially available in 2020. The millimeter wave bands such as 28GHz is being used to achieve the peak rate of 10 [Gbps] or higher targeted for 5G. In the late 2020s, low latency and high-capacity data transmission over both the up and down links will become important. This is because 5G will be utilized in the late 2020s, and telemedicine and teleoperation using 4K/8K and other high-definition video transmission will become widespread. In this study, we propose a relaying method that converts frequency multiplexing into spatial multiplexing during relaying, with the goal of achieving low latency and high capacity relaying communications. The user equipment, base stations, and relay stations have different conditions in terms of transmission power and number of antennas. Therefore, the proposed method achieves high capacity by frequency multiplexing in the link where the number of antennas is limited. In addition, the proposed method uses spatial multiplexing to achieve high capacity while suppressing the increase in resource usage in the link where multiple antennas are available. The 39 GHz band, which has more frequency resources than the existing 5G bands, is used for the evaluation in the link of frequency multiplexing. Then, the 28 GHz band, which is used commercially in 5G, is used for the evaluation in the link of spatial multiplexing. For low latency relaying, analog circuits are used during the relaying process to convert between frequency-multiplexed and space-multiplexed signals without modulation and demodulation, while maintaining the number of multiplexes. In this paper, simulation evaluations show that the proposed method improves the communication distance where the throughput exceeds 4 [Gbps] to 6.5 times that of 39 GHz band 5G communications without relaying, and to 1.3 times that of RF repeaters in conventional relaying methods that use the 39 GHz band both before and after relaying, indicating that the uplink communication distance can be extended.

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.

Digital predistortion (DPD) using neural network(NN) has attracted attention as a promising technique for compensating complex nonlinear distortions caused by wideband radio frequency power amplifiers. However, NN-DPD is difficult to implement in hardware due to the large number of multiplications. In this paper, we propose an NN-DPD using DeepShift that replaces neural network multiplications with bitwise shift and sign flipping during both training and inference. First, when DeepShift was applied, we showed that applying residual learning to DPD can reduce performance degradation. Next, we examined pre-trained models using floating-point baseline models and scratch-trained models and found that the pre-trained models perform better in exchange for requiring pretraining. The scratch model, on the other hand, has the advantage of improving the training computational complexity. In an actual experiment of passing a signal with DPD applied through a power amplifier at 2.14 GHz, the error vector magnitude was slightly degraded from 1.31 % trained by the floating-point model to 1.57 % with the pre-trained model and 1.90 % with the scratch model.

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.

In this paper, we present a new technique to identify the main inter-cell uplink interferers at each base station of a cellular network simultaneously. In the proposed scheme, for the duration of the interferer identification process, the scheduling of all users to uplink transmission resources is determined by a central controller. The scheduling is carefully chosen so that each cell can determine the contribution of each interferer from measurements of total received interference power by inversion of the interferer scheduling matrix. The advantages of the proposed scheme are as follows. Firstly, it can be adapted to any physical layer and take advantage of a central controller (a widely expected 5G feature). Secondly, it can be operated in both time-division duplex (TDD) and frequency-division duplex (FDD) modes and scales well with the network density. Finally, all the complexity is placed on the base station side, thus reducing the pressure on the user terminal's hardware. The effectiveness of the method is demonstrated mathematically and its performances are investigated by means of Monte-Carlo simulations. Results show that the proposed technique performs as well as the conventional method based on quantized reports of downlink measurements used in LTE-Advanced.

Due to the recent mobile traffic explosion, especially in hotspots where a lot of traffic is generated in a limited space, an imbalance in the traffic load among cells occurs. Such traffic load imbalance requires the deployment of many base stations (BSs) in the hotspots. This results in the inefficient operation of cellular systems. In order to overcome this problem, load balancing techniques have been studied. There are two main approaches. One is based on antenna tilt control (TiLt-control-based Load Balancing: TL-LB) and the other is based on handover (HO) timing control (HandOver-timing-control-based Load Balancing: HO-LB). These techniques have been studied separately; however, a detailed comparison of the two techniques has not been reported so far. This paper numerically compares TL-LB and HO-LB in terms of not only the amount of user equipment (UE) distribution to neighboring cells, but also the degradation of Signal to Interference plus Noise Power Ratio (SINR), HO performance, and the change in the number of UEs in each cell. Numerical results show that TL-LB is superior to HO-LB in terms of the amount of UE distribution, SINR, and HO performance. With TL-LB, about 65% of UEs in a cell are distributed to neighboring cells with little degradation of SINR and HO performance. In contrast, with HO-LB, no more than 43% of UEs can be distributed to neighboring cells at the expense of 5th percentile SINR degradation of 21 dB and significant degradation in HO performance. The results also show that TL-LB is inferior to HO-LB in terms of the change in the number of UEs in each cell. However, this disadvantage of TL-LB can be overcome by appropriate cooperation control with neighboring cells. It can be concluded that TL-LB with appropriate cooperation control is much more effective than HO-LB as a load balancing technique.

SC-FDMA is a multiple access technique adopted in LTE uplink transmission. SC-FDMA, as well as OFDMA, can improve system throughput by assigning different frequency portions of system bandwidth to different user equipments (UEs) adaptively based on their channel quality information. In SC-FDMA uplink cellular system, interference arising from UEs in neighboring cells degrades system performance, especially the performance of the cell-edge UE. To overcome this drawback, many papers propose the Fractional Frequency Reuse (FFR) technique and analyze its influence. However, these studies have different conclusions on the effectiveness of FFR, and the throughput gain of FFR depends on the evaluating conditions. In this paper, we reveal the conditions where FFR is effective by demonstrating the throughput gain of FFR in SC-FDMA uplink cellular system. In order to analyze the throughput gain of FFR, we focus on the performance relationship between FFR and frequency selective scheduling (FSS). From the analysis, the throughput gain of FFR is small because of the degradation of the FSS gain, and FFR is effective when the following conditions are met; (i) the number of UEs is small, (ii) the multipath delay spread is large or equal to 0.

In IEEE 802.16 uplinks, the permutation of data on a frequency domain is performed when logical subchannels are transformed to physical subchannels to scatter deterioration of the radio propagation environment via inter-cell interference. However, it is ideal from the viewpoint of evading interference to completely divide the radio resource between the interfering cells, when there is comparatively low traffic among the cells. In this paper, the authors propose a novel concept for the definition of bursts in the uplinks. It is that a BS defines UL bursts for an imaginary user. Based on this idea, the BS can assign subchannels to users while generating blank logical subchannels between used logical subchannels in uplinks, which could not be realized in conventional ideas. Furthermore, the authors propose a radio resource allocation scheme to successfully avoid inter-cell interference based on this concept. Numerical analysis showed that the proposed method suppresses the interference between adjacent cells and that throughput performance can improve.

In this paper, we propose a burst construction and packet mapping scheme in the orthogonal frequency-division multiple access (OFDMA) downlinks of IEEE 802.16 systems. In the standard of the systems, there are some restrictions on the usage of downlink radio resources and they are defined in the physical layer (PHY) specification. One of the main restrictions is that a rectangular region, which is called Burst, is defined on a two-dimensional domain of time and frequency, and packets must be allocated within the region. However, how to define the burst and allocate data packets within the burst is left implementation dependent. Therefore, we consider a burst construction and packet mapping scheme that is easy to realize on systems and attains efficient usage of radio resources. By computer simulation, it has been confirmed that the proposed scheme can decrease not only the control data ratio within the rectangles, but also control data that must be transmitted at the head of every frame, which can result in higher throughput.

In this paper, we propose a Quality of Service (QoS) packet scheduling algorithm for the IEEE 802.16 system and evaluate the algorithm on hardware. The proposed algorithm controls packets based on a deadline for each packet determined from QoS parameters. The feature of our algorithm is that it can simply control the various QoS classes defined in the standard using the deadline. As a result, QoS parameters, such as minimum traffic rate and maximum latency, would be guaranteed by scheduling packets based on the deadline. We then implemented and evaluated the algorithm with a testbed. There have been few reports of an evaluation on IEEE 802.16 hardware before. We confirmed that QoS with high priority is guaranteed with the proposed algorithm.

In this paper, we present a multihop mobile communications system utilizing fixed relay stations to enhance the coverage of the service area. A multihop mobile communications system was originally proposed as a system in which only mobile stations relay communication between other mobile stations and a base station to solve the problem of poor coverage expected in a future cellular system. In the system, however, a probability of a mobile station being able to connect with a base station via their relay functions is governed by a probability of mobile stations existing in its neighborhood. Therefore, we introduce fixed stations as relay stations in addition to relay functions of mobile stations to enhance the probability of link conductivity between a base station and a mobile station. By adopting this multihop architecture, a mobile station which needs relay functions can choose relay stations among fixed relay stations or other mobile stations. Moreover, we also examine its time slot allocation methods. We confirmed that the ratio of successful connection between mobile stations and base stations improved about 0.15 by introducing just 4 fixed relay stations in a certain environment

It is expected that vehicles have on-board terminals to communicate with the fixed network and also with surrounding vehicles in the near future. This paper explains our developed inter-vehicle P2P (peer-to-peer) communication experimental on-board terminal, we nicknamed it "V-ant", which has the feature of adopting AODV (ad-hoc on-demand distance vector) protocol, control of AODV packets transmission direction using GPS data, and adaptive media switching between vehicle-to-vehicle ad-hoc communication, communication through access points, and PHS (personal handy-phone system in Japan).

This paper presents novel broadcast methods for an inter-vehicle communications system for ITS. During inter-vehicle communications, it is essential that emergency information be broadcast to surrounding vehicles. Emergency information such as emergency-vehicle-approach information and traffic accident information are needed by vehicles in a particular area. By limiting the broadcast direction, the proposed methods can provide broadcasts to a particular area and avoid mistakenly notifying other areas where the information is not needed. We developed a simulation and experimental system for the proposed method and confirmed that the information is broadcast to the desired area.

This paper proposes a multihop mobile communications system using an MC-CDMA transmission technique in the forward links. In the system, it is assumed henceforth that each mobile station has a relaying function where a frequency band is used both in the 1st and 2nd hops. Then, we examined a radio resource allocation method in the forward links. In this method, an orthogonal codeword is assigned for each group that consists of one or more mobile stations and the resources of time and frequency are distributed to each link within a group. To clarify the characteristics of this method, we compared the proposed system and a conventional one without relaying, in terms of the base station connection rate, throughput, and so on. Simulation results indicate that the proposed system improves the connection rate without further orthogonal codeword consumption, and user throughput in an appropriate mobile station density.

This paper deals with symbol timing synchronization issue for broadband OFDM based mobile communication system. A symbol timing approach using adaptive threshold is proposed. Several OFDM training symbol formats and the corresponding timing metrics are evaluated. Simulations am carried out using Rayleigh fading model at 5 GHz band, with the velocity being 100 km/h and 150 km/h. The performances of the simulation results demonstrate that the proposed symbol timing method is feasible for mobile telecommunications.