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Due to recent explosion of data volume and velocity, a new array of lightweight key-value stores have emerged to serve as alternatives to traditional databases. The majority of these storage engines, however, sacrifice their read performance in order to cope with write throughput by avoiding random disk access when writing a record in favor of fast sequential accesses. But, the boundary between sequential vs. random access is becoming blurred with the advent of solid-state drives (SSDs). In this work, we propose our new key-value store, LogStore, optimized for hybrid storage architectures. Additionally, introduce a novel cost-based data staging model based on log-structured storage, in which recent changes are first stored on SSDs, and pushed to HDD as it ages, while minimizing the read/write amplification for merging data from SSDs and HDDs. Furthermore, we take a holistic approach in improving both the read and write performance by dynamically optimizing the data layout, such as deferring and reversing the compaction process, and developing an access strategy to leverage the strengths of each available medium in our storage hierarchy. Lastly, in our extensive evaluation, we demonstrate that LogStore achieves up to 6x improvement in throughput/latency over LevelDB, a state-of-the-art key-value store

This paper considers a Gaussian multiple-access channel with random user activity where the total number of users $\ell_n$ and the average number of active users $k_n$ may grow with the blocklength $n$. For this channel, it studies the maximum number of bits that can be transmitted reliably per unit-energy as a function of $\ell_n$ and $k_n$. When all users are active with probability one, i.e., $\ell_n = k_n$, it is demonstrated that if $k_n$ is of an order strictly below $n/\log n$, then each user can achieve the single-user capacity per unit-energy $(\log e)/N_0$ (where $N_0/ 2$ is the noise power) by using an orthogonal-access scheme. In contrast, if $k_n$ is of an order strictly above $n/\log n$, then the capacity per unit-energy is zero. Consequently, there is a sharp transition between orders of growth where interference-free communication is feasible and orders of growth where reliable communication at a positive rate per unit-energy is infeasible. It is further demonstrated that orthogonal-access schemes in combination with orthogonal codebooks, which achieve the capacity per unit-energy when the number of users is bounded, can be strictly suboptimal. When the user activity is random, i.e., when $\ell_n$ and $k_n$ are different, it is demonstrated that if $k_n\log \ell_n$ is sublinear in $n$, then each user can achieve the single-user capacity per unit-energy $(\log e)/N_0$. Conversely, if $k_n\log \ell_n$ is superlinear in $n$, then the capacity per unit-energy is zero. Consequently, there is again a sharp transition between orders of growth where interference-free communication is feasible and orders of growth where reliable communication at a positive rate is infeasible that depends on the asymptotic behaviours of both $\ell_n$ and $k_n$. It is further demonstrated that orthogonal-access schemes, which are optimal when $\ell_n = k_n$, can be strictly suboptimal., 50 pages. Submitted to the IEEE Transactions on Information Theory. Revised paper according to the comments by the reviewers. This article supersedes our previous submissions arXiv:1904.11742 and arXiv:2005.07436

In this brief, we present FracTCAM, an efficient methodology for ternary content addressable memory (TCAM) emulation on Xilinx field-programmable gate arrays (FPGAs) by leveraging primitive architectural resources. The proposed methodology exploits the fracturable nature of lookup table random access memories (LUTRAMs) and built-in slice flip-flops for deeper pipelining. Multiple slices can be combined together to build deeper and wider TCAMs using ANDing operations. This results in TCAM implementations that achieve lower resources utilization, lower delay, and power consumption. A comparison with the existing schemes shows that FracTCAM consistently achieves the best performance per area (PA) and performance per area per watt (PAW).

IEEE 802.11ax partitions a regular 20MHz channel into smaller sub-channels called resource units to support simultaneous multiuser operation using orthogonal frequency division multiple access (OFDMA). Uplink OFDMA random access (UORA) in IEEE 802.11ax allows stations to transmit via a scheduled random access mechanism. UORA is initiated via a trigger frame which aside from serving as a synchronization mechanism, also informs stations which resource units are allowed for random access. Using the trigger frame information, the stations engage in an OFDMA backoff process to win access to a resource unit. Similar to slotted ALOHA, the maximum normalized throughput of UORA is only 37 percent due to high probability of collisions at high loads. To reduce collisions, we equip UORA with carrier sensing capability resulting in a new uplink hybrid UORA (H-UORA) OFDMA access mechanism. Unlike other multi-carrier CSMA methods previously proposed in literature, H-UORA is an easily implementable modification to current 802.11ax WLANs. We show that H-UORA can achieve a normalized throughput of at least 80 percent (which increases further depending on the buffering capabilities of the access point) using various numerical analysis and simulations.

Unslotted ALOHA protocol has been adopted as a channel access mechanism in commercial low-power wide-area networks (LPWANs), such as Sigfox and long-range (LoRa) alliance. This work examines the throughput and random access (RA) delay distribution of unslotted ALOHA systems by considering exponential random backoff (ERB) or uniform random backoff (URB) algorithm. We further characterize the operating region of the systems as unsaturated stable, bistable, and saturated regions in terms of the new packet arrival and retransmission rates. To run the system stably with the maximum throughput, we propose a Bayesian online backoff algorithm that estimates the number of backlogged devices. Its performance is compared with other algorithms, such as particle filter (PF)-based algorithm, binary exponential backoff (BEB) algorithm, and the algorithm of exploiting exact backlog size information. Through extensive simulations, it is demonstrated that the performance of the proposed algorithm is very close to the upper bound and robust to time-varying traffic condition.

This paper investigates the noise covariance estimation problem for the networked linear system under communication protocol. To reduce network burden and avoid data collisions, the data transmission from the sensor nodes to the filter is scheduled by the random access protocol (RAP), where only a sensor node is permitted to send data at each transmission instant. Within the framework, the correlations of the innovations data are utilized to construct a least-squares problem to estimate the noise covariances, where a novel estimation form of the auto-covariance from measurement data is given. Moreover, the two cases of the process noise correlated and uncorrelated with the measurement noise are both discussed. It can be proved that the estimated covariances are unbiased and asymptotically converge to the true values as the sample size increasing. Finally, the simulation results are given to demonstrate the effectiveness of the presented method.

The massive machine-type communications (mMTC) is one of the three generic services for 5G. With the connection-based random access (CBRA) scheme, each machine-type device (MTD) establishes a connection with the base station (BS) prior to its data transmission. Due to the explosive growth of the number of MTDs, many MTDs would establish connections with the BS, which necessitates the study on how to efficiently manage the massive connections with MTDs. To address this issue, in this article, we propose a unified utility-based analytical framework for the optimal connection management of mMTC in 5G networks, where the signaling overhead for connection establishment, access delay, and the connection resource utilization ratio is included. Specifically, we first derive key performance metrics, i.e., the mean time length of each connection and resource utilization ratio, as functions of traffic input rate and inactivity timer. By further considering the signaling overheads and access delay of each MTD, the network utility is formulated and maximized by optimally choosing the inactivity timer. We then present a detailed discussion on the effect of system parameters on the optimal inactivity timer and the corresponding maximum network utility. Finally, we extend the analytical framework to the scenario in which the CBRA scheme coexists with the packet-based random access scheme, i.e., transmitting packets in the random access channel without connection establishment. The critical threshold in terms of the traffic input rate is characterized, which sheds important light on the access scheme selection issue.

In this paper, we consider an Internet-of-Things (IoT) network that supports two types of devices, namely static devices (SDs) and dynamic devices (DDs), with multiple channels. The notion of power-domain non-orthogonal multiple access (NOMA) is applied so that DDs can not only dynamically change channels, but also transmit signals with a high power to reduce collisions with SDs. Thus, learning algorithms are needed for DDs to select channels. To this end, multiarmed bandit (MAB) algorithms can be used. However, in general, it is difficult to analyze the performance of MAB algorithms in the setting due to the interaction of DDs. Thus, in this paper, instead of analyzing MAB algorithms, we formulate an evolutionary game of DDs in NOMA-based IoT networks and analyze the evolutionary game. It is shown that the game has a unique evolutionary stable state (ESS) in terms of the channel selection probability. The performance according to the ESS can be regarded as a baseline performance, because it is the best performance achieved by the DDs competing for multiple channel resources. Thus, the resulting baseline performance can be used to see if a learning algorithm including MAB can achieve a better performance by avoiding competition between DDs.

The adoption of wireless communications and, in particular, Wi-Fi, at the lowest level of the factory automation hierarchy has not increased as fast as expected so far, mainly because of serious issues concerning determinism. Actually, besides the random access scheme, disturbance and interference prevent reliable communication over the air and, as a matter of fact, make wireless networks unable to support distributed real-time control applications properly. Several papers recently appeared in the literature suggest that diversity could be leveraged to overcome this limitation effectively. In this paper a reference architecture is introduced, which describes how seamless link-level redundancy can be applied to Wi-Fi. The framework is general enough to serve as a basis for future protocol enhancements, and also includes two optimizations aimed at improving the quality of wireless communication by avoiding unnecessary replicated transmissions. Some relevant solutions have been analyzed by means of a thorough simulation campaign, in order to highlight their benefits when compared to conventional Wi-Fi. Results show that both packet losses and network latencies improve noticeably., preprint, 13 pages (Winner of the "2017 Best Paper Award for the IEEE Transactions on Industrial Informatics")

We consider a relay-aided Slotted ALOHA solution for uplink random access for an Optical Wireless Communications (OWC)-based Internet of Things (IoT). The first phase of uplink, the one between IoT devices and the relays, is realized using indoor OWC, while the second phase, between the relays and a base station, represents the long-range RF transmission based on low-power wide area network such as LoRaWAN and occurs outdoors. The throughput performance dependence on the OWC and RF channel conditions is observed. The behavior of the performance gain due to adding relays is highlighted and investigated under different channel and traffic conditions., Published in: 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

In the context of massive machine-type communications (mMTCs), the narrowband Internet-of-Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored random access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This article presents the first data-driven analysis of NB-IoT RA, exploiting a large-scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators' configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. A comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a machine learning (ML) approach and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in an RA-enhanced scheme and show that optimized configurations enable power consumption reduction of at least 50%. We also make our data set available for further exploration, toward the discovery of new insights and research perspectives.

In Press/En prensa In this article, we develop a modeling framework to describe the uplink behavior of radio access in a sliced cell, including most features of the standard 3GPP multiple access procedures. Our model allows evaluating throughput and latency of each slice, as a function of cell parameters, when resources are in part dedicated to individual slices and in part shared. The availability of an accurate model is extremely important for the automated run time management of the cell and for the correct setting of its parameters. Indeed, our model considers most details of the behavior of sliced 5G cells, including Access Class Barring (ACB) and Random Access CHannel (RACH) procedures, preamble decoding, Random Access Response (RAR), and Radio Resource Control (RRC) procedures. To cope with a number of slices devoted to serve various co-deployed tenants, we derive a multi-class queueing model of the network processor. We then present (i) an accurate and computationally efficient technique to derive the performance measures of interest using continuous-time Markov chains, which scales up to a few slices only, and (ii) tight performance bounds, which are useful to tackle the case of more than a fistful of slices. We prove the accuracy of the model by comparison against a detailed simulator. Eventually, with our performance evaluation study, we show that our model is very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices for services with different characteristics and performance requirements, such as machine type communications and human type communications. This work is partially supported by the Region of Madrid through the TAPIR-CM project (S2018/TCS-4496), by a Ramon y Cajal grant (ref: RYC-2014-16285) from the Spanish Ministry of Economy and Competitiveness, and by HOME (Hierarchical Open Manufacturing Europe) project supported by the Regione Piemonte, Italia (framework program PORFESR 14/20). This work was supported by the EU Commission through the 5GROWTH project (grant agreement no. 856709).

Uplink power domain non-orthogonal multiple access (NOMA) random access (RA) system allows users to transmit their packet with one of target receive power (TRP) levels at the base station (BS). Then the BS can separate the users in simultaneous transmission with successive interference cancellation (SIC), when the users choose different TRP levels. It enjoys a higher throughput through supporting simultaneous transmissions that occur frequently in RA system. This letter examines how to incorporate uplink NOMA RA system with the existing Long-Term Evolution (LTE) RA procedure with minimal modifications. Furthermore, to optimize the proposed RA procedure, we propose an online control algorithm for backoff interval and analyze its performance particularly with focus on the average RA delay. The results show that the proposed RA procedure achieves $0.5896{L}$ of throughput for a total of ${L}$ RA preambles and guarantees the average RA delay by 1/( $0.5896{L} -\lambda $ ) for Poisson traffic with mean $\lambda $ .

With the increasing proliferation of radars on vehicles, interference among vehicular radars is becoming a serious issue. In this work, we consider a frequency modulated continuous wave (FMCW) radar network with coherent radar interference where all radars adopt the same chirp slopes. Four asynchronous non-cooperative media access control (MAC) protocols are used for mutual radar interference mitigation. We propose two cross-layer performance metrics - multiple access capacity and probability of target misdetection to quantify the network performance under each MAC protocol. Based on our analysis and extensive simulations, we find the pure random access achieves a very poor trade-off between multiple access capacity and probability of target misdetection. However, such a trade-off can be significantly improved by frequency hopping and phase coding. This shows that proper MAC protocols can achieve very good performance even without synchronization and coordination. We describe new insights behind such gains that can be valuable for FMCW radar MAC design.

Wireless communications can facilitate transfer of synchrophasor data between spatially separated phasor measurement units (PMUs) and phasor data concentrators (PDCs). However, such communication systems may impose random access delay and failure on PMU channels that lead to missing synchrophasor data frames at the output of the PDC. This paper presents a new approach for online reliability assessment and improvement of synchrophasor data communications. The proposed approach involves elaborate estimation and probabilistic prediction algorithms that trigger a prioritized handover mechanism in order to minimize the number of synchrophasor data frames that are missing over successive time stamps. Extensive simulations based on the LTE communications in a low-voltage distribution feeder confirm significant performance improvement and fast fault detection under the communication link failures. This non-intrusive approach can be adopted by network operators to ensure reliable transmission of synchrophasor data to monitoring, control, and protection applications in smart grids.

Growing demand for the Internet of Things (IoT) applications including smart cities, healthcare systems, smart grids, and transportation systems, has enhanced the popularity of Machine-Type Communication (MTC) in 5G and 6G cellular networks significantly. Massive access is a well-known challenge in MTC that should be efficiently managed. In this paper, a grant-based massive access mechanism is introduced where time-frames are separated into two distinct parts; one for contention-based resource granting and the other for scheduled data transmission. In the contention period, we propose a novel random access mechanism where the nodes are grouped based on their distances from the Base Station (BS), and the access probability of each group member is adjusted through solving an optimization problem. In the proposed mechanism, energy efficiency, spectrum efficiency and access delay are formulated in terms of the access probability of devices using p-persistent CSMA method. Thereafter, some optimization problems are formulated to improve the energy/spectrum efficiency and access delay by adjusting the access probability of devices while regarding their delay requirements. The simulation results indicate that the proposed method is better than the previous ones considering energy efficiency, access delay, bandwidth efficiency, and scalability. Also, in the proposed method, delay-sensitive nodes experience lower access delay.

Random access (RA) protocols are suitable for the support of massive connectivity especially in machine-type communications, such as the Internet of Things (IoT). In this context, ALOHA protocols (both slotted and unslotted) have been re-introduced as promising enablers due to a potentially high throughput when enhanced via successive interference cancellation (SIC). Meanwhile, SIC has become the most popular solution for RA detection problems. In this letter, we first point out the weaknesses of SIC and then propose a novel method based on joint detection of all randomly scheduled data packets. The proposed method is based on the joint computation of a-posteriori probabilities for all data symbols and packet localizations, such that optimal detection for RA is obtained.

In this paper, we study a method for compensating the non-translational motion behavior in video coding. The proposed method models the motion field of a prediction block based on the motion information of the neighboring blocks by using a linear regression approach. In order to provide a finer granularity of motion vectors the Regression-based Motion Vector Field (RMVF) method derives the motion field in ${4}\times {4}$ sub-block accuracy. Such approach generates a smooth and more realistic motion vector field inside the prediction block. The motion field generated with RMVF is then used as a new merge mode along with other merge modes in VTM-2.0 test model of the Versatile Video Coding (H.266/VVC) standard. The conducted experiments with JVET CTC sequences illustrate that the proposed RMVF method provides 0.77%, 0.19% and 0.41% bitrate reductions with random access (RA), low delay B (LDB) and low delay P (LDP) configurations, respectively. Furthermore, this method provides on average 0.65% bitrate saving for the 360° sequences in equirectangular projection format (ERP) with RA configuration.

The bright future of smart cities relies on an effective deployment of IoT technologies. Machine-type communications (MTC) is a major backbone technology that supports connectivity for the Internet of things (IoT). Cellular networks are known to be cost-effective, with ubiquitous coverage that ease the deployment of MTC. However, cellular networks were originally designed for human-centric services with high-cost devices and ever-increasing rate requirements. In contrast, MTC services need to support low-cost, low-energy, massive number of devices. This poses a number of challenges toward the adaptation of current cellular networks to accommodate MTC. This article gives an overview of the conventional random access (RA) scheme of cellular networks and its variants in the literature. However, without discounting the efforts of optimizing the RA scheme, we show that due to the increased collisions and prohibitive overhead, it falls short to support MTC with reduced latency and guaranteed reliability. Alternatively, we discuss different uplink access techniques that are found promising in tackling massive connectivity while avoiding the shortcomings of the conventional RA. Moreover, we discuss how to utilize different future 5G and beyond technologies to efficiently handle massive MTC while pointing out the promising role of machine learning techniques.

We investigate unsourced and grant-free massive random access in which all the users employ the same codebook, and the basestation is only interested in decoding the distinct messages sent. To resolve the colliding user packets, a novel approach relying on user transmissions with random-like amplitudes selected from a large number of possible signatures is proposed. The scheme is combined with convolutional coding for error correction. The receiver operates by first identifying the signatures used by the transmitting nodes employing a sparsity-based detection algorithm, and then utilizing a trellis-based decoding algorithm. Despite its simplicity, the proposed solution offers excellent performance.

Nowadays, the rapid development of artificial intelligence (AI) provides a fresh perspective in designing future wireless communication systems. Innumerable attempts exploiting AI methods have been carried out, which results in the state-of-the-art performance in many different areas of wireless communications. In this article, we present the most recent and insightful developments that demonstrate the potentials of AI techniques in different physical layer (PHY) components and applications including channel characterization, channel coding, intelligent signal identification, channel estimation, new PHY for random access in massive machine-type communication (mMTC), massive multiple-input multiple-output (MIMO) power control and PHY resource management. Open challenges and potential future directions are identified and discussed along this research line.

In this article, we study a random access (RA) scheme to alleviate the RA channel (RACH) overload problem in the massive-machine-type communication (mMTC) environment. We first propose a timing advance-based preamble resource expansion (TAPRE) scheme which effectively increases preamble resources and reduces the preamble collision probability by adjusting preamble transmission timing with timing advance (TA) information. We also propose a resource allocation wait (RAW) scheme which efficiently reduces the number of RA failures due to the lack of physical uplink shared channel (PUSCH) resources. We then propose an overall procedure for enhanced RA with TAPRE and RAW (ERATAR). In addition, we provide the analysis of RA performance by applying more practical assumptions than the existing analysis. We validate our analysis with the system level simulation based on NS-3, and compare the various performances of our ERATAR to those of existing works. Numerical results show that our analysis provides more accurate results than the existing work and our ERATAR provides significantly improved performances compared with those of existing works.

Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff–Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

In this article, we propose a two-tier communication strategy to facilitate data collection in unmanned aerial vehicle (UAV)-enabled massive Internet of Things (IoT) systems through introducing ground access points (APs) to serve between the UAV and IoT devices. In the first tier of our proposed strategy, all IoT devices transmit their packets to their local APs via a multi-channel ALOHA-based random access scheme, while in the second tier, APs deliver their aggregated data to the UAV through coordinated time division multiple access. Thus, our introduced APs not only liberate the UAV from the potential massive IoT congestion but also facilitate the design of UAV’s trajectory based on the location of APs. To examine the performance of our strategy, we propose a tractable framework to analyze the average system throughput. We reveal that the average two-tier throughput of each AP monotonically increases with its maximum achievable throughput in the second tier, while the increasing slope becomes steeper with a higher traffic load mean in the first tier. Then, we formulate the joint design of UAV’s trajectory and resource allocation as a non-convex optimization problem to maximize the average system throughput while considering the heterogeneous quality of service requirement of each AP. To solve this problem, a low-complexity iterative algorithm is devised based on successive convex approximation. Numerical results demonstrate the substantial average system throughput gain achieved by our proposed strategy and design in the context of massive access, compared to the baseline schemes in the literature.

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive $p$-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. To learn an optimal control policy, we first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy among UAVs in which the altitude of UAVs and channel access probability of IoT devices are dynamically and jointly controlled to attain the maximal long-term network capacity while maintaining energy sustainability of UAVs. The proposed algorithm outperforms Deep RL based solutions with reward shaping to account for energy costs, and achieves a temporal average system capacity which is $82.4\%$ higher than that of a feasible DRL based solution, and only $6.47\%$ lower compared to that of the energy-constraint-free system., Comment: Submitted to IEEE JSAC Special Issue on Massive Access for 5G and Beyond

Narrowband Internet of Things (NB-IoT) is a new technology developed to support low-power wide area networks (LPWAN) services. To extend its coverage and decrease its transmission power, devices in one NB-IoT cell are divided into several coverage enhancement (CE) levels with different random access configuration. This potentially results in unfair access, especially when massive number of devices in different CE levels simultaneously accessing the network. This work presents an effective strategy to configure the random access in NB-IoT to yield fair performance across CE levels. An analytical model is used to estimate the performance of each CE level and overall system performance in term of normalized throughput and average access delay. Simulation is incorporated to verify the accuracy of the model. Different practical assumptions of fair system are explored and examined in the experiment. The result shows that the analytical model is accurate under various loads. Additionally, the proposed search strategy is proven to be able to obtain the configuration which yield acceptable throughput fairly for all CE levels.

Grant-free random access is a key enabler in massive machine-type communications (mMTC) to reduce signalling overhead and latency thereby improving the energy efficiency. One of its main challenges lies in joint user activity identification and channel estimation (JUICE). Due to the sporadic mMTC traffic, JUICE can be solved as a compressive sensing (CS) problem. We address CS-based JUICE in uplink with single-antenna transmitters and a multiantenna base station under spatially correlated fading channels. We formulate a novel CS problem that utilizes prior information on the second order statistics of the channel of each user to improve the performance. We propose a method based on alternating direction method of multipliers to solve the JUICE efficiently. The simulation results show that the proposed method significantly improves the user identification accuracy and channel estimation performance with lower signalling overhead as compared to the baseline schemes.

There is a growing interest in the transition from 4-step random access to 2-step random access in machine-type communication (MTC), since 2-step random access is well-suited to short message delivery in various Internet of Things (IoT) applications. In this paper, we study a 2-step random access approach that uses code division multiple access (CDMA) to form multiple channels for data packet transmissions with a spreading factor less than the number of channels. As a result, the length of data transmission phase in 2-step random access can be shorter at the cost of multiuser interference. To see how the decrease of the length of data transmission phase can improve the spectral efficiency, we derive the throughput as well as the spectral efficiency. From the results of analysis, we can show that the 2-step CDMA-based random access approach can have a higher spectral efficiency than conventional 2-step approach with orthogonal channel allocations, which means that the performance of MTC can be improved by successfully transmitting more data packets per unit time using CDMA. This is also confirmed by simulation results., 11 pages, 10 figures, IEEE Trans. Communications (to appear)

First-in–first-out (FIFO) line buffers occupy considerable logic gates and consume significant power of the feature from accelerated segment test (FAST) image corner detection engine. In this study, a FAST-Chip (FAST-C) engine is proposed to eliminate these drawbacks. It comprises three core techniques: arch pixel estimation (APE), hardware-efficient line buffer (HLB), and interleaving-based parallel access (IPA). APE can save three line buffers using gradient information for pixel prediction. With APE, the number of line buffers can be saved by 33%. HLB effectively organizes static random access memories (SRAMs) as line buffers to store reference pixels rather than FIFO. It demands less power and fewer logic gates. IPA makes the FAST-C engine have regular data paths and smooth data scheduling, keeping hardware throughput. Experiment results show that the accuracy difference between FAST and FAST-C is as minor as 2.29%. The proposed FAST-C engine is implemented using the Taiwan Semiconductor Manufacturing Company (TSMC) 0.18- $\mu \text{m}$ CMOS process with a logic gate count of 223k, where the line buffers are included. Its frequency is 613 MHz with a power consumption of 1.18 W. Its maximum throughput can sufficiently support $3840\times 2.160$ (4k) @ 60 frames per second (fps). Compared with sophisticated FAST engines, the FAST-C engine presents outstanding energy efficiency (EE) and area efficiency (AE).

Efficient energy transfer is crucial for a green base station with limited energy to replenish the energy of stand-alone devices in the Internet of Things (IoT). Cognitive radio (CR) is critical to satisfying the spectrum requirement for IoT devices. This paper proposes two wireless charging policies, namely, charging in residual multi-frame time and charging one energy unit at a time, for the random access multiple access control on CR-based green-energy networks. The novelty of the policies is to send wireless energy at appropriate timing and duration. Event-driven simulations are conducted to study the performances of the policies. Simulation results show that the two policies significantly yield high energy efficiency, and maintain throughput and quality-of services. Besides, the policy of charging one energy unit at a time outperforms the policy of charging in residual multi-frame time.

We propose a low-complexity unsourced random access (URA) transmission scheme where the data payload, encoded by an error-reduction code, undergoes repetition, permutation, and spreading. An approach to perform spatial graph coupling of the URA messages via randomized message delays is also presented. The proposed system, in either block or coupled form, outperforms the existing URA counterparts in the high multi-user interference regime and provides a substantial increase in the supported multi-user loads. A choice of stronger, albeit more complex, error-correction code allows for performance improvements at lower system loads. Finally, system performance predictions via a state-evolution analysis are also demonstrated.

In this article, we study the receiver design for the uplink transmission of a human-type communications (HTC) and machine-type communications (MTC) (H&M) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO and signal sparsity of MTC packets. With the probability model, we propose to conduct joint device activity identification, channel estimation, and signal detection. We develop a message-passing-based statistical interference framework to systematically and efficiently solve the joint estimation problem for the H&M coexisted massive MIMO system. Specifically, we propose two receiver schemes based on time-slotted and non-time-slotted grant-free random access for massive machine-type device connectivity. We show that, by exploiting the channel and signal sparsity, our proposed message-passing-based algorithms significantly outperform the conventional training-based approaches in which the device activity state and the channel are estimated by sending pilots prior to data transmission, and are able to approach the genie bound with known signal support in the relatively high signal-to-noise (SNR) regime. Last but not least, we show that there exists a significant gain in terms of the number of admissible devices in the system by allowing H&M coexistence, as compared to orthogonal transmission approaches in which different time/frequency slots are assigned to HTC and MTC services.

For fast access to the cell, we propose an enhanced random access scheme exploiting multiple transmissions of random access preambles and random access responses. The proposed random access scheme decreases the latency of the random access procedure, by avoiding random access retrials from the beginning of the random access procedure due to the expiry of a random access response window. We also provide a model and analysis of random access latency with the average delay and the failure probability of random access procedure. The model of random access latency considers the collisions of random access preambles transmitted by multiple mobile stations to a base station as well as the error of random access response corresponding to the received random access preamble transmitted by the base station to the mobile station. Our numerical results in accordance with the model show that the proposed scheme provides lower latency.

Random access channel (RACH) procedures in modern wireless communications are generally based on multi-channel slotted-ALOHA, which can be optimized by flexibly organizing devices' transmission and retransmission. However, due to the lack of information about the traffic generation statistics and the occurrence of random collision, optimizing RACH in an exact manner is generally challenging. In this article, we first summarize the general structure of optimization for different RACH schemes, and then review existing RACH optimization methods based on machine learning (ML) and non-ML techniques. We demonstrate that the ML-based methods can better optimize RACH schemes compared to non-ML-based methods due to their capability in solving high-complexity long-term optimization problems. To further improve the RACH performance, we introduce a decoupled learning strategy (DLS) for access control optimization, which individually executes two sub-tasks: traffic prediction and access control configuration. In detail, the traffic prediction relies on an online supervised learning method adopting recurrent neural networks that can accurately capture traffic statistics over consecutive frames, while the access control configuration uses either a non-ML-based controller or a cooperatively trained deep reinforcement learning (DRL)-based controller selected depending on the complexity of different random access schemes. Numerical results show that the DLS optimizer considerably outperforms conventional DRL optimizers in terms of higher training efficiency and better access performance.

In this paper, we investigate jointly sparse signal recovery and jointly sparse support recovery in Multiple Measurement Vector (MMV) models for complex signals, which arise in many applications in communications and signal processing. Recent key applications include channel estimation and device activity detection in MIMO-based grant-free random access which is proposed to support massive machine-type communications (mMTC) for Internet of Things (IoT). Utilizing techniques in compressive sensing, optimization and deep learning, we propose two model-driven approaches, based on the standard auto-encoder structure for real numbers. One is to jointly design the common measurement matrix and jointly sparse signal recovery method, and the other aims to jointly design the common measurement matrix and jointly sparse support recovery method. The proposed model-driven approaches can effectively utilize features of sparsity patterns in designing common measurement matrices and adjusting model-driven decoders, and can greatly benefit from the underlying state-of-the-art recovery methods with theoretical guarantee. Hence, the obtained common measurement matrices and recovery methods can significantly outperform the underlying advanced recovery methods. We conduct extensive numerical results on channel estimation and device activity detection in MIMO-based grant-free random access. The numerical results show that the proposed approaches provide pilot sequences and channel estimation or device activity detection methods which can achieve higher estimation or detection accuracy with shorter computation time than existing ones. Furthermore, the numerical results explain how such gains are achieved via the proposed approaches., Comment: 16 pages, 12 figures, to appear in JSAC

This letter addresses the uplink radio resource allocation problem in a 5G massive Machine Type Communication (mMTC) scenario, where a large number of MTC devices generate small-sized packets. The current cellular networks are unsuitable for this scenario, due to the limited resources allocated to the Physical Random Access Channel (PRACH) and to the Physical Uplink Shared Channel (PUSCH). To overcome this issue, instead of improving the succeeded access attempts in the PRACH, as widely addressed in literature, we focus on the PUSCH. We present the innovative idea to exploit the unused PUSCH resources to serve also the MTC devices that have failed their access attempt, by assigning them a pool of resources, in a contention-based mode. The simulation results show that the proposed PUSCH Resource Reallocation Algorithm improves significantly any static and dynamic system performance in terms of number of succeeded communications, at the cost a negligible increment of energy consumption.

We introduce a modulation for unsourced massive random access whereby the transmitted symbols are rank-1 tensors constructed from Grassmannian sub-constellations. The use of a low-rank tensor structure, together with tensor decomposition in order to separate the users at the receiver, allows a convenient uncoupling between multi-user separation and single-user demapping. The proposed signaling scheme is designed for the block fading channel and multiple-antenna settings, and is shown to perform well in comparison to state-of-the-art unsourced approaches.

Internet of Things (IoT) imbued with several heterogeneous services and applications is integral to the ultimate realization of connected living. However, massive connectivity would pose huge challenges not only due to immense scalability but also due to diverse characteristics of periodicity, delay-criticality, and quality-of-service (QoS) requirement. One important trend of this diversity is that the connected devices would manifest different priorities. Random access procedure (RAP) is the first step by which most devices establish connection to the base station. A major transformation is urgently needed in RAP for cellular IoT as the volume of connected devices would far exceeds human oriented connections of the legacy networks. To prioritize the RAP for IoT devices, we propose novel online control algorithm that enables the most likely successful preamble transmission for the devices that manifest higher priority requirements. In the proposed algorithm, the number of active devices in each priority is recursively estimated based on Bayesian rule and then the preambles to each priority are accordingly allocated. We extend our proposal by adopting access class baring (ACB) to optimize the algorithm and subsequently, enhance it further by incorporating access delay requirements. Extensive simulations show the effectiveness of proposed algorithms over multiple priorities and confirm that the proposed algorithms are able to resolve congestions for massive activation of IoT devices.

The future mobile system shall be able to support satellite access to provide an ubiquitous wireless connection service. Satellite communication is vulnerable to spoofing attacks due to the open nature of wireless channels, especially for the downlink satellite system information signalling (SIS) which has not been authenticated yet before initial access. SIS spoofing attacks would cause interferences as well as access failures or even denial of services (DoS) to the legitimate users. Thanks to the knowledge of satellite ephemeris as well as the global navigation satellite system (GNSS), Doppler frequency shift as an available physical attribute can be exploited to identify the source of SIS before the random access procedure. A physical layer authentication (PLA) approach based on Doppler frequency shift is proposed to determine whether the received SIS is from the legitimate communication satellite. Simulations show that the performance of the proposed method outperforms the channel state information (CSI) based PLA scheme.

In this paper, we study grant-free random access with massive multiple input multiple output (MIMO) systems. We first show that the performance of massive MIMO based grant-free random access is mainly decided by the probability of preamble collision. The implication of this is that although the number of antennas can be arbitrarily large, the (average) number of successful packet transmissions can be limited by the number of preambles. Then, we propose an approach to preamble collision reduction without increasing the number of preambles using the notion of superpositioned preambles (S-preambles). Since the channel estimation for conjugate beamforming can be carried out when unused S-preambles are known, a simple approach is derived to detect unused S-preambles and its performance is analyzed for a special case (superpositions with 2 preambles). Based on analysis and simulation results, it is confirmed that the proposed approach with S-preambles can increase the success probability with the same spectral efficiency as the conventional approach., Comment: 11 pages, 11 figures, to appear in IEEE Trans. Wireless Communications

Wireless random access protocols are attracting a revived research interest as a simple yet effective solution for machine-type communications. In the quest to improve reliability and spectral efficiency of such schemes, the use of multiple receivers has recently emerged as a promising option. We study the potential of this approach considering a population of users that transmit data packets following a simple slotted ALOHA policy to a set of uncoordinated relays ( phase-1 ). These, in turn, independently forward – part of – what decoded towards a collecting sink ( phase-2 ). For an on-off fading channel model, we provide exact expressions for phase-1 throughput and packet loss rate for an arbitrary number of relays, characterising the benefits of multi-receiver schemes. Moreover, a lower bound on the minimum amount of phase-2 resources needed to deliver all information collected at the relays is provided. The bound is proven to be achievable via random linear coding when no constraints in terms of latency are set, with an overhead that approaches zero with the inverse of the packet length. We complement our study discussing a family of simple forwarding policies that require no packet-level coding, and optimising their performance based on the amount of available phase-2 resources. The behaviour of both random linear coding and simplified policies is also characterised when receivers are equipped with finite buffers, revealing non-trivial tradeoffs.

In this letter, a novel spatial group based access class barring (ACB) scheme is proposed for machine-to-machine (M2M) random access system with timing advance (TA) assisted machine-type devices (MTDs) identification and preamble reuse. Specifically, multiple ACB factors are assigned for groups to provide precise access control, and an optimization problem of determining the optimal ACB factors which maximize the throughput is formulated. To reduce the computational complexity, the optimal problem is further simplified to get a suboptimal solution. The performance advantages of the proposed scheme are verified by simulations.

The grant-free non-orthogonal multiple access (GF-NOMA) system, in which users autonomously transmit uplink data signals without carrying out a complicated random access process, offers significant advantages for increasing the efficiency of resource usage with low signaling overhead. In the proposed GF-NOMA system, since the collision resolution process is omitted and resources are not pre-assigned to users, multiple users may use same resource, without the base station (BS) being able to identify these collided users. In addition, in the absence of the UL synchronization procedure generally performed in the random access process, multiple users’ signals are received asynchronously. To solve these problems, we have developed a new transceiver structure for the GF-NOMA system consisting of a secondary preamble and a multiuser-multisymbol (MUMS) detector. The purpose of the secondary preamble is to successfully detect user access and estimate channel information even when no collision resolution process is being performed. The MUMS receiver is then proposed as a way to effectively mitigate the severe interference caused by the asynchronous transmission. Simulation results show that the proposed preamble structure and the receiver structure are superior to the conventional schemes. Furthermore, we show that the GF-NOMA system with the proposed transceiver structure achieves much better performance than other GF-NOMA systems in terms of reliability and successfully detected bits.

Grant-free (GF) access has been one of the enablers for the various use cases in 5th generation (5G) mobile system, especially for time-critical massive machine-type communication (mMTC). However, these use cases have diverse quality of service (QoS) requirements, which can be measured in terms of an access success rate from a GF random access perspective. Consequently, a GF scheme that enables supporting of diverse QoS is highly sought. This article proposes a GF access scheme in which high-QoS users superpose multiple preambles to improve their access success rate as a result of the diversity in access collision and multiple access interference seen by multiple preambles. We further show that in the presence of multiple antennas in the base station (BS), a low-complexity receiver can correctly detect active preambles with a significantly high probability, even under severe multiple access interference caused by non-orthogonal preamble transmission. A theoritical performance analysis is conducted by modeling the preamble reception as a multiple measurement vector-based compressive sensing problem. The preamble misdetection probability is shown to decrease exponentially as the number antennas at BS increases. Numerical results demonstrate multiple-order improvement in terms of the access success rate for critical-QoS users, even under severe noise and multiple access contamination.

In 5G and beyond wireless scenarios, it is expected to support tremendous amount of communicating machines. With an exponential increase of machine-to-machine (M2M) system deployments, efficient approaches to massive access by a large number of devices are to be studied. In this paper, we propose a massive multiple-input multiple-output (MIMO) based grant-free random access (RA) with resolution of preamble collision for massive access. Based on the channel hardening and favorable propagation characteristics of massive MIMO, collided signals processed at the base station (BS) can be viewed as a variation of superposition modulation, and are to be recovered by successive interference cancellation (SIC) techniques. Besides, taking into consideration the effect of pass loss and fractional power control (FPC), analytic expressions of success probability of the proposed collision resolution with conjugate beamforming (CB) and zero-forcing beamforming (ZFB) are derived. With simulation results, we verify the analyses and show that the proposed protocol can resolve most preamble collisions.

Random Access techniques are many, most of which are designed for a limited number of mobile nodes. However, in a 5G Internet of Things environment, the design of random access must accommodate a very large number of devices which are heterogeneous in nature, and packets are in general short and come in a variety of lengths. Variable lengths of short packets may waste bandwidth if each packet transmits at the beginning of a time slot. In this paper, packet squeezing allows for a packet to make a selection between aligning to the beginning and aligning to the ending of a time slot. Two or more nodes transmitting at the same time slot may not result in a collision if two conditions are met simultaneously. First, one node selects an alignment mode different from that of the remaining nodes. Secondly, the sum of the time durations of two “representative” packets is shorter than the duration of a time slot. The performance analysis demonstrates that the throughput gain due to the packet squeezing is significant. The gain of packet squeezing can be enhanced by time slot management, where a transmitting node randomly selects a set of points of Finite Projective Plane and transmits at the time slots corresponding to the points of the chosen set. Due to the rather uniform distribution of the transmitting nodes and fewer empty time slots, the gain of packet squeezing is further improved for the entire frame. Based on the simulation results and analysis, we have observed that the time slot management extends the range of packet arrival rate of peak throughput for packet squeezing. Lastly, we compare and contrast the combining of packet squeezing and time slot management with well-known random access protocols of IoT such as slotted ALOHA and NOMA, and simulate real systems to verify the results of performance analysis.

In this study, a new approach of random access and pilot allocation in crowded scenarios of massive multi-input multi-output (MIMO) systems is proposed. When the number of users in a cell is more than number of orthogonal pilots, random access to pilots (RAP) is a promising solution to solve the massive access problem of users; though the collision of pilots is a by-product of this method. There are several methods for pilot collision resolution such as strongest user collision resolution (SUCR), but they have one major limitation; i.e. they cannot serve users more than the number of orthogonal pilots simultaneously in coherent transmission mode. In this paper, angle of arrival (AOA) and angular spread (AS) of the received pilots in a compressed sensing approach are examined by applying sparsity of massive MIMO channels in angular domain. That allows to allocate same orthogonal pilot to non-overlapped users in spatial domain for coherent data transmission. By applying this approach in newly proposed protocol (SBPA), the limitation in number of adopted simultaneous users in crowded scenarios of a massive MIMO system would be resolved. Numerical results show improvement in access failures rate compared to SUCR in crowded massive MIMO systems.

Convolutional neural networks (CNNs) exhibit state-of-the-art performance while performing computer-vision tasks. CNNs require high-speed, low-power, and high-accuracy hardware for various scenarios, such as edge environments. However, the number of weights is so large that embedded systems cannot store them owing to their limited on-chip memory. A different method is used to minimize the input image size, for real-time processing, but it causes a considerable drop in accuracy. Although pruned sparse CNNs and special accelerators are proposed, the requirement of random access incurs a large number of wide multiplexers for a high degree of parallelism, which becomes more complicated and unsuitable for FPGA implementation. To address this problem, we propose filter-wise pruning with distillation and block RAM (BRAM)-based zero-weight skipping accelerator. It eliminates weights such that each filter has the same number of nonzero weights, performing retraining with distillation, while retaining comparable accuracy. Further, filter-wise pruning enables our accelerator to exploit inter-filter parallelism, where a processing block for a layer executes filters concurrently, with a straightforward architecture. We also propose an overlapped tiling algorithm, where tiles are extracted with overlap to prevent both accuracy degradation and high utilization of BRAMs storing high-resolution images. Our evaluation using semantic-segmentation tasks showed a 1.8 times speedup and 18.0 times increase in power efficiency of our FPGA design compared with a desktop GPU. Additionally, compared with the conventional FPGA implementation, the speedup and accuracy improvement were 1.09 times and 6.6 points, respectively. Therefore, our approach is useful for FPGA implementation and exhibits considerable accuracy for applications in embedded systems.

In this paper, we propose a novel convolutional neural network (CNN) architecture, MFRNet, for post-processing (PP) and in-loop filtering (ILF) in the context of video compression. This network consists of four Multi-level Feature review Residual dense Blocks (MFRBs), which are connected using a cascading structure. Each MFRB extracts features from multiple convolutional layers using dense connections and a multi-level residual learning structure. In order to further improve information flow between these blocks, each of them also reuses high dimensional features from the previous MFRB. This network has been integrated into PP and ILF coding modules for both HEVC (HM 16.20) and VVC (VTM 7.0), and fully evaluated under the JVET Common Test Conditions using the Random Access configuration. The experimental results show significant and consistent coding gains over both anchor codecs (HEVC HM and VVC VTM) and also over other existing CNN-based PP/ILF approaches based on Bjontegaard Delta measurements using both PSNR and VMAF for quality assessment. When MFRNet is integrated into HM 16.20, gains up to 16.0% (BD-rate VMAF) are demonstrated for ILF, and up to 21.0% (BD-rate VMAF) for PP. The respective gains for VTM 7.0 are up to 5.1% for ILF and up to 7.1% for PP.

In this paper, we consider an asynchronous random access scheme called irregular repetition ALOHA (IRA) as a generalization of contention resolution ALOHA (CRA) with varying repetitions. We present an asymptotic performance analysis of CRA and IRA on the collision channel for regular and irregular repetition rates. We also propose an improvement by merging the clean parts of packet replicas in partial collisions, and extend our analysis to this scenario as well. Specific designs of repetition distributions based on the new analysis show that the optimized solutions of irregular repetition slotted ALOHA (IRSA) perform well in both IRA and the enhanced scheme, and they considerably outperform the regular repetition distributions. We also introduce energy harvesting (EH) to both schemes as a practical and sustainable adaptation, where users are able to harvest energy and store it in their finite-capacity batteries. We model the battery state by a discrete-time Markov chain and derive an optimal transmission policy to maximize the asymptotic performance of the system. We provide comprehensive numerical results for both practical and asymptotic scenarios to verify the validity of the proposed analyses, and illustrate the benefits of the proposed systems.