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The adoption of one-bit analog-to-digital converters (ADCs) in wireless communication has become increasingly popular, owing to their potential to reduce power consumption. However, there is a decreased spectral efficiency (SE) associated with their use, as they are unable to take advantage of high-modulation-order signaling. Faster-than-Nyquist (FTN) signaling along with oversampling are promising means of improving SE at the expense of additional inter-symbol interference (ISI). This study establishes a common platform for design and evaluation by providing a general system model and problem formulation, as well as proposing an alternative solution framework for the one-bit quantization system. By systematically evaluating optimizable factors that impact performance, such as signaling rate, transmit sequences, and error correction blocks, this study proposes a deep learning (DL)-based architecture, specifically channel autoencoders, for end-to-end communication over a one-bit quantization channel. Various transceiver designs are proposed for performance comparison as well as to enable one-bit quantized communication at previously unattainable information rates through conventional means. Numerical results showcase the superiority of jointly optimizing all blocks in an additive white Gaussian noise (AWGN) channel. The DL-based scheme operationalizes Bit Error Rate (BER) performance at information rates scaling up to 80% of Shamai’s limit. Beyond AWGN, the autoencoder-based transceiver design extends to make one-bit quantization viable over a challenging Rayleigh multipath fading channel. A detailed analysis compares a pilot-based scheme for one-bit quantization with a pilotless option, thereby revealing their SE and BER relationship. The pilot-based quantized scheme outperforms conventional fading channel transmission without quantization by up to 10 dB.

The amount of traffic in wireless networks is increasing exponentially, and this problem can be mitigated using device-to-device (D2D) caching technology, which installs a cache on a mobile end device. Devices can reduce the cell load through self-offloading via content in their own cache and D2D offloading using content in others' caches. However, especially in the early stage of D2D caching systems, a limited number of devices with a small storage might be used, and it is required to develop a caching scheme with excellent performance despite the small cache size. Regarding content popularity, which is common to most users, the preference probability values are not concentrated on some pieces of content, making it difficult to achieve satisfactory performance using a small cache. On the other hand, when considering individual users, content preferences may contain large values for specific content based on individual characteristics. In addition, the performance can be improved by considering short-term content preferences that reflect changes in content preferences over time or newly created content during peak hours. In this article, the hit ratio is divided into six parts considering self- and D2D offloading, common and individual user preferences, and little and large temporal changes in content preferences during peak hours. We also conceptually divide the cache of a helper into six areas in relation to the six parts of the hit ratio, and discuss cache partitioning and proactive caching strategies according to the environment.

Dynamic time-division duplexing (TDD) allows for flexible resource allocation for the varying downlink (DL) and uplink (UL) traffic demands in the 5G cellular network. It becomes more useful when the number of active users is highly limited, especially for the small cells in a ultra-dense network. However, traffic imbalance between DL and UL in adjacent cells incurs crosslink interference (CLI), which may seriously reduce the system throughput. In order to deal with CLI induced by dynamic TDD, full or partial information on slot assignment and/or mutual interference must be shared among all cells for coordinating dynamic slot assignment throughout the system. In this paper, we propose the fully decentralized slot assignment policies that can reduce the CLI without resorting to side information shared among the cells. It is based a bi-directional slot-ordered scheduling scheme in each subframe, which exploits implicit location-dependent information such as signal-to-interference-plus-noise ratio (SINR) and/or three-dimensional spatial angle for all UEs in each cell under a full-dimension multi-input and multi-output (FD-MIMO) operation. Our system-level performance evaluation in 5G system scenario has shown that the proposed approach can maintain total interference almost constant even subject to the CLI. Furthermore, it achieves the average system throughput as much as 95% and 91% of the performance that can be achieved by a centralized greedy search for DL and UL, respectively, without much compromising the lower 5% system throughput.

5G K-SimSys is a system-level simulator which has been designed and implemented to provide an open platform and a tractable testbed for evaluating the system-level performance of 5G standard. In this paper, we present its design overview with the overall modular structure, including the functionality of the flexible modules. Meanwhile, massive multi-input multi-output (MIMO) is a key technology for improving the spectral efficiency of 5G systems. While massive MIMO has been standardized in 3GPP Rel-13 (LTE-Advanced Pro), the next generation of massive MIMO standard is now available in Rel-16, a.k.a New Radio (NR) interface, with further improvement by introducing more antenna elements over the higher frequency band. In particular, the beam-based air interface for above 6 GHz band involves various antenna configurations and feedback schemes, requiring a more complex testbed for system-level performance evaluation. This paper examines the multi-antenna technologies in 3GPP NR specification to develop its system-level model in 5G K-SimSys in order to test the factors affecting performance in the corresponding environments through various embodiments. In the simulation results, the baseline performance for massive MIMO in NR is evaluated by changing the number of antenna ports and the number of spatial multiplexing layers in different experimental environments. Particularly, the effect of vertical beamforming on interference is evaluated for Full Dimension MIMO (FD-MIMO). In order to demonstrate that 5G K-SimSys is an easy-to-design simulator that facilitates modification and reconfiguration, owing to its modularized and customized structure, we consider the proposed bandwise analog beamforming (BAB) scheme. The modular and flexible structure immediately allows implementation of virtual modules for bandwise analog beamforming by reusing the elementary modules as hierarchically designed simulator objects.

We present a new spatiotemporal offloading (STO) framework that can deal with the time-varying traffic demand in an overloaded location using proactive caching during off-peak times. Focusing on a conventional cellular network, the system-wide mobility can be captured by the inter-cell transitions along individual users' mobility pattern. It is opposed to the existing geographical caching for content placement during off-peak times, which treats a system built around multiple base stations as a single service area, thus limiting its performance to low hit probability. We formulate a system-level STO problem to determine a content placement strategy that minimizes the access link load by delivering the requested data through device-to-device (D2D) links, rather than through a cellular link, particularly targeting potentially overloaded cells. Because its complexity is seriously attributed to the individual users' cell-by-cell mobility and content preferences, an aggregated flow model has been proposed to reduce the search space by invoking the law of segment conservation. Simulation results clearly demonstrate the effect of the spatiotemporal offloading that can be realized through inter-cell mobility-aware proactive caching. An extremely important implication of the current framework is that a varying traffic demand can be spatiotemporally distributed in a cellular system, overcoming the traditional limitation that arises from conservative or inefficient resource allocation due to a reliance on over-dimensioning.

In existing dynamic spectrum sharing (DSS) systems, each node arbitrates channel access independently based on carrier-sensing mechanisms such as the listen-before-talk (LBT) protocol. Owing to the uncoordinated channel access between all nodes, the channel is occupied in a random pattern. This makes it difficult to reduce the mismatch in channel quality indicator (CQI) while increasing the spatial reuse gain between the different nodes; therefore, the areal capacity gain obtained by adding nodes is much lower in existing DSS systems than in the cellular system. In this paper, we propose a different means of improving the areal capacity for downlink DSS systems. It is a group-wise DSS approach that ensures full frequency reuse in each group of base stations (BSs) by performing LBT only between the representative BSs, each of them selected by each group. Once a channel is secured by each group, all its member BSs share a channel simultaneously. This approach makes closed-loop feedback-based link adaptation practical while boosting spatial reuse gain even in the DSS environment. To implement the group-wise DSS, we propose a single unified framework that employs elementary algorithms for BS grouping and carrier-sensing threshold adjustment. Our system-level simulation results demonstrate that the proposed framework boosts the areal capacity gain by approximately 4.42 times as much as the conventional approach.

The increase of mobile traffic volume in a wireless network using high carrier frequency can lead to coverage and backhaul bottleneck issues, and these issues can be solved by the use of device-to-device (D2D) caching technology that installs a content cache on a device and uses D2D communication to supply contents to other devices. In this paper, a special device that can supply contents using D2D communication to other devices is called a helper, while a device that can receive contents from a helper is called user equipment (UE). Even though a helper may have a large storage capacity compared to a UE, the cache capacity of a helper is still limited, making it difficult to obtain a sufficient hit ratio using a caching scheme based on the long-term content popularity of a large number of UEs while considering the distant future. In this paper, to solve the problem of the conventional D2D caching system, we suggest instead updating the cache contents based on the short-term content preference of individual user for the near future. In the proposed caching scheme, a UE in an area with insufficient data supply predicts the content that will be consumed in the near future. This information is then passed to a mobile helper moving along a predictable travel path, such as a bus and a self-driving car that is passing through or will pass though an area with sufficient data supply. Then, the mobile helper prefetches the contents and moves to update the cache of the nomadic or fixed helper near the UE. The simulation results show that content prefetching considering each UE and the near future can achieve better performance than content caching considering a large number of UEs and the distant future.

In the case of broadband wireless communication systems with high carrier frequencies, high-speed wireless communication is feasible, but if high-capacity backhaul is not supplied to all base stations, a backhaul bottleneck may occur. To solve this backhaul bottleneck problem, one can use wireless caching or device-to-device (D2D) caching technology that installs a cache on a mobile end device, use the cache to store video content, and then supply the video content when requested by other end devices. Prior to peak traffic hours, video content can be stored on mobile end devices according to the content preferences, but it is difficult to solve cell overload problems that occur during peak traffic hours. This is because peak traffic hours can persist for a long time, and thus device positions, content preferences, and cell loads are liable to change during peak traffic hours. Providing broadband radio and high-capacity backhaul to all cells can create cost issues, but if there are some cells with broadband wireless resources and high-capacity backhaul, these cells may often support sufficient data even during peak traffic hours. If a mobile end device can intermittently enter a cell with sufficient data supply, it can update its cache using some short-term information. In this paper, we compare wireless caching schemes that update the caches of mobile end devices during peak traffic hours with the goal of reducing overload data. If high-capacity base stations are installed within a certain distance interval from each other to reduce the content update period, then good performance can be obtained by frequently updating the caches while considering the short-term content preferences of nearby overload cells. In addition, if a mobile device can predict its movement path, performance can be further improved by considering only the overload cells in the predicted path.

In sparse code multiple access (SCMA)-based grant-free (GF) access, both user's channel and the selected SCMA codebook (CB) are considered as a transmission signature for multi-user detection (MUD). In conventional SCMA-based grant-free (GF) access where a single codebook (CB) is employed per a user's random access transmission, however, random selection of CBs and random fading channel reduce the minimum Euclidean distance among overloaded layers (signatures). This significantly degrades the performance. Furthermore, it is also challenging to reduce the collision rate in the course of selecting random access resources while maintaining low preamble overhead. In this paper, we consider a new approach that maps user's consecutive symbols to codewords from the different SCMA CBs in a round-robin manner. It is shown that a combinatorial nature of multiple CBs and the inherent code diversity therein allow to simultaneously reduce collision probability while providing sufficient separation among transmissions, consequently decreasing users' activity misdetection rate. It is also shown that the proposed multi-codebook SCMA (MC-SCMA)-based GF access scheme outperforms the conventional single CB-based SCMA version by overloading of more users (almost up to 200%) as it allows a large number of highly non-orthogonal preambles to be employed while keeping the same preamble length (overhead). Finally, we conclude that the proposed joint design of non-orthogonal preambles and data transmission based on multiple CBs provides a significant performance improvement for the grant-free access system. It can also readily be extended to other code-based non-orthogonal multiple access (NOMA) schemes.

In licensed-assisted access using the LTE (LAA) standard, carrier sensing via the listenbefore-talk (LBT) procedure is a vital feature for fair sharing with the Wi-Fi systems. Furthermore, it has been designed to support frequency reuse-1 operation among all cells by the virtue of licensed spectrum. As opposed to the two existing channel-access schemes for frequency reuse-1, transmission start time alignment (TSTA) and energy detection threshold adaptation (EDTA), which may not be able to maximize the LAA system throughput without violating the requirement of fair coexistence, we propose a new frequency-reuse-1 scheme, referred to as the alignment reference interval adaptation-based LAA (ARIALAA). It attempts to combine the advantages of TSTA and EDTA into a unified access framework, in which the alignment reference interval (ARI) is adaptively adjusted to control the channel-access probability for LAA and Wi-Fi systems. Meanwhile, to operate the ARIA-LAA effectively toward our design objective, we design the fuzzy Q-learning system that adapts the continuous variable ARI to the dynamically changing wireless network environment. Based on the analytical system models and formation of the optimization problem, it employs a model-free learning algorithm that interacts with the state, defined as the current level of fairness achieved by adaptation, and the Q-learning function to determine the ARI as a global action. Our simulation results demonstrate that the ARIA-LAA is a novel scheme of spectrum sharing with spatial reuse for LAA that enhances the overall system capacity while satisfying the fair-coexistence requirement in the unlicensed spectrum.

The 3GPP standardized the physical layer specification in 5G New Radio to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) coexistence in usage scenarios including aerial vehicles (AVs). Dynamic multiplexing of URLLC traffic was standardized to increase the outage capacity. DM allocates a fully overlapped bandwidth part (BWP) of eMBB and URLLC AVs to perform the immediate scheduling of URLLC traffic by puncturing ongoing eMBB traffic. However, DM often suffers from a significant frame error incurred by puncturing. Meanwhile, BWP can be sliced orthogonally for eMBB and URLLC AVs, possibly preventing overdimensioning the resources depending on the eMBB and URLLC traffic loads. In this paper, we propose a dynamic BWP allocation scheme that switches between two multiplexing methods, dynamic multiplexing (DM) and orthogonal slicing (OS), so as to minimize an impact of uRLLC traffic on eMBB traffic. To implement efficient BWP allocation, the capacity region is analyzed by considering the effect of physical layer parameters, such as modulation and coding scheme (MCS) levels and code block group size on DM and OS. OS is effective for improving the eMBB throughput under a URLLC latency constraint for deterministic and predictable URLLC traffic, whereas DM has limited error-correcting capability against the URLLC’s puncturing effect. The relative MCS level of eMBB and URLLC is critical in determining the eMBB traffic tolerance against puncturing. Identifying the performance tradeoff between DM and OS, the tolerance level is quantified by a URLLC load threshold. It is given in an approximate closed form, which is an essential reference for selecting DM over OS, enabling dynamic BWP allocation for the URLLC AV.

In this paper, we present the design and evaluation of a software-based wireless power transfer platform that enables the development of a prototype involving various open- and closed-loop power control functions. Our platform is based on a loosely coupled planar wireless power transfer circuit that uses a class-E power amplifier. In conjunction with this circuit, we implement flexible control functions using a National Instruments Data Acquisition (NI DAQ) board and algorithms in the MATLAB/Simulink. To verify the effectiveness of our platform, we conduct two types of power-control experiments: a no-load or metal detection using open-loop power control, and an output voltage regulation for different receiver positions using closed-loop power control. The use of the MATLAB/Simulink software as a part of the planar wireless power transfer platform for power control experiments is shown to serve as a useful and inexpensive alternative to conventional hardware-based platforms.

One of the key requirements for next generation wireless or cellular communication systems is to efficiently support a large number of connections for Internet of Things (IoT) applications, and uplink non-orthogonal multiple access (NOMA) schemes can be used for this purpose. In uplink NOMA systems, pilot symbols, as well as data symbols can be superimposed onto shared resources. The error rate performance can be severely degraded due to channel estimation errors, especially when the number of superimposed packets is large. In this paper, we discuss uplink NOMA schemes with channel estimation errors, assuming that quadrature phase shift keying (QPSK) modulation is used. When pilot signals are superimposed onto the shared resources and a large number of devices perform random accesses concurrently to a single resource of the base station, the channels might not be accurately estimated even in high SNR environments. In this paper, we propose an uplink NOMA scheme, which can alleviate the performance degradation due to channel estimation errors.

One of the most important processes in device-to-device communications of cellular devices is that of discovery, which determines the proximity of devices. When a discovery process is performed, there are several parameters to determine, including the discovery range, the discovery period, and the modulation and coding scheme of the discovery messages. In this paper, we address the relationships between these parameters and describe an optimization framework to determine them. In the proposed procedure, it is important to first optimize the discovery rate, which is defined as the number of discoverable devices per unit time. Once the discovery rate is maximized, the discovery period can be determined accordingly based on the device density and the target discovery range. Since the discovery rate is not affected by many of discovery parameters such as the discovery range, the device density, and the discovery period, it can be used as a performance metric for comparing discovery schemes with different discovery ranges or different discovery periods.

In broadband wireless Orthogonal Frequency Division Multiple Access (OFDMA) systems where a set of subcarriers are shared among multiple users, the overall system throughput can be improved by a band-AMC mode that assigns each suband, a set of contiguous subcarriers within a coherence bandwidth, to individual user with the better channel quality. As long as channel qualities for the subbands of all users are known a priori, multiuser and multiband gains can be simultaneously achieved with opportunistic scheduling. This paper presents an analytical means of evaluating the maximum system throughput for a bandadaptive modulation and coding (AMC) mode under the various system parameters. In particular, the practical features of resource management for OFDMA system are carefully modeled within the current analytical framework. Our numerical results demonstrate that band-AMC mode outperforms the diversity mode only by providing the channel qualities for a subset of good subbands, confirming the multiuser and multiband diversity gain that can be achieved by the band-AMC mode.

A multihop relay (MR) and repeater are useful means for improving system throughput and coverage in a cellular mobile packet access system, as the carrier-to-interference ratio can be improved when deploying them in a heavily shadowed region. In this paper, we report on our investigation of bandwidth efficiency and the associated service outage performance for different relay scenarios, using system level simulation for a cellular Orthogonal Frequency Division Multiple Access-Time Division Duplexing (OFDMA-TDD) system. We have demonstrated that network throughput gain by typical optical repeaters, which have a simple amplify-and-forwarding capability in a full-duplexing mode, could be minimal in open space subject to cochannel interference from all repeaters in the neighboring cells. This is true, even though they are generally useful for warranting the outage performance with a multiple order of combining gain, especially in the destructive area, for example, basements or indoors with heavy wall attenuation, that naturally shields interference. Meanwhile, we show that multihop relays increase the average system capacity (almost doubling the system throughput) by fully reusing the frequency in every relay station, while improving the per-user data rate in the cell edges or improving the outage performance in the heavily shadowed areas.