Random Access Performance Research Articles

Joint altitude and beamwidth optimization for LEO satellite‐based IoT constellation

SummaryLow Earth Orbit (LEO) satellite‐based Internet of Things (IoT) has become a hot topic in IoT networks due to the ability of global coverage, especially in remote areas. How to design a commercial LEO satellite‐based constellation to meet the IoT traffic requirement remains an open problem. In this paper, we propose a throughput optimization algorithm for LEO satellite‐based IoT networks meanwhile reducing the number of LEO satellite. Based on stochastic geometry theory, a closed‐form expression is derived for the throughput of a dynamic LEO satellite‐based IoT networks when LEO satellite equips with capture effect (CE) receiver and successive interference cancelation (SIC) receiver, respectively. Furthermore, a joint altitude and beamwidth optimization problem is formulated under the constraint of Walker constellation to optimize the throughput and the number of LEO satellites. To solve this multi‐objective optimization problem, we design an iterative non‐dominated sorting genetic algorithm II (NSGA‐II) for the rapid development of IoT traffic. Simulation results show that our proposed algorithm can effectively improve the throughput performance of random access (RA) protocol in LEO satellite‐based IoT networks compared to benchmark problems.

Dynamic Beam-Based Random Access Scheme for M2M Communications in Massive MIMO Systems

Internet of things, supported by machine-to-machine (M2M) communications, is one of the most important applications for the 6th generation (6G) systems. A major challenge facing by 6G is enabling a massive number of M2M devices to access networks in a timely manner. Therefore, this paper exploits the spatial selectivity of massive multi-input multi-output (MIMO) to reduce the collision issue when massive M2M devices initiate random access simultaneously. In particular, a beam-based random access protocol is first proposed to make efficient use of the limited uplink resources for massive M2M devices. To address the non-uniform distribution of M2M devices in the space and time dimensions, an Markov decision process (MDP) problem with the objective of minimizing the average access delay is then formulated. Next, we present a dynamic beam-based access scheme based on the double deep Q network (DDQN) algorithm to solve the optimal policy. Finally, simulations are conducted to demonstrate the effectiveness of the proposed scheme including the model training and random access performance.

A Map Tile Data Access Model Based on the Jump Consistent Hash Algorithm

Tiled maps are one of the key GIS technologies used in the development and construction of WebGIS in the era of big data; there is an urgent need for high-performance tile map services hosted on big data GIS platforms. To address the current inefficiency of massive tile map data management and access, this paper proposes a massive tile map data access model that utilizes the jump consistent hash algorithm. Via the uniformity and consistency of a certain seed of a pseudo-random function, the algorithm can generate a storage slot for each tile data efficiently. By recording the slot information in the head of a row key, a uniform distribution of the tiles on the physical cluster nodes is achieved. This effectively solves the problem of hotspotting caused by the monotonicity of tile row keys in the data access process, thereby maximizing the random-access performance of a big data platform and greatly improving concurrent database access. Experiments show that this model can significantly improve the efficiency of tile map data access by more than 39% compared to a direct storage method, thereby confirming the model’s advantages in accessing massive tile map data on a big data GIS platform.

Random access sequence set design in wireless cellular communication networks

In modern wireless cellular communication systems, Zadoff–Chu (ZC) sequences have been chosen as random access sequences due to their perfect autocorrelation and cross-correlation properties. However, the orthogonality between these sequences is no longer true when there is a frequency offset between the access device and access point. Thereby, the overall performance of ZC sequence-based random access signals will be limited. In this paper, we give a comprehensive analysis on the effect of frequency offset. We show how frequency offset affects the orthogonal property between sequences, and hence incurs the “energy attenuation” and “energy leakage” problems. Consequently, the detection probabilities of the transmitted random access sequences and the false alarm probabilities of the unsent sequences are affected. A frequency offset resistant detection metric is proposed in this paper to improve the detection probability. In order to resist the frequency offset effect as well as apply to the proposed detection metric, mathematical properties of the optimized roots and cyclic shift set are exploited. Finally, we illustrate how to construct a practical random access signal set under the given timing and frequency offset uncertainties. This analytical framework provides a useful insight into ZC sequence-based random access signal design and performance analyses in wireless cellular communication systems.

Physical random access signal design for 5G mobile satellite communication systems

In view of the distinctive characteristics of satellite communication, the physical random access signals used in the terrestrial mobile communication system have to be modified or redesigned for the satellite communication system. In this paper, we boost the random access signal energy by repeating the short Zadoff–Chu (ZC) sequence based preamble signal used in the terrestrial system. Different long ZC sequences are used to scramble this cascaded sequence to distinguish different random access signals for multiple random access user equipments. For correlation performance optimization, properties of the roots for both the short and long ZC sequences are mathematically analyzed and derived. Finally, we illustrate how to construct a root set for these different long ZC sequences based on the obtained propositions in a practical way. This analytical framework provides a useful insight into ZC sequence-based random access signal design and performance analysis in mobile satellite communication systems.

Effective Radio Resource Allocation for IoT Random Access by Using Reinforcement Learning

<p>Emerging intelligent and highly interactive services result in the mass deployment of internet of things (IoT) devices. They are dominating wireless communication networks compared to human-held devices. Random access performance is one of the most critical issues in providing quick responses to various IoT services. In addition to the anchor carrier, the non-anchor carrier can be flexibly allocated to support the random access procedure in release 14 of the 3rd generation partnership project. However, arranging more non-anchor carriers for the use of random access will squeeze the data transmission bandwidth in a narrowband physical uplink shared channel. In this paper, we propose the prediction-based random access resource allocation (PRARA) scheme to properly allocated the non-anchor carrier by applying reinforcement learning. The simulation results show that the proposed PRARA can improve the random access performance and effectively use the radio resource compared to the rule-based scheme. </p> <p> </p>

TreeLine

Many modern key-value stores, such as RocksDB, rely on log-structured merge trees (LSMs). Originally designed for spinning disks, LSMs optimize for write performance by only making sequential writes. But this optimization comes at the cost of reads: LSMs must rely on expensive compaction jobs and Bloom filters---all to maintain reasonable read performance. For NVMe SSDs, we argue that trading off read performance for write performance is no longer always needed. With enough parallelism, NVMe SSDs have comparable random and sequential access performance. This change makes update-in-place designs, which traditionally provide excellent read performance, a viable alternative to LSMs. In this paper, we close the gap between log-structured and update-in-place designs on modern SSDs with the help of new components that take advantage of data and workload patterns. Specifically, we explore three key ideas: (A) record caching for efficient point operations, (B) page grouping for high-performance range scans, and (C) insert forecasting to reduce the reorganization costs of accommodating new records. We evaluate these ideas by implementing them in a prototype update-in-place key-value store called TreeLine. On YCSB, we find that TreeLine outperforms RocksDB and LeanStore by 2.20× and 2.07× respectively on average across the point workloads, and by up to 10.95× and 7.52× overall.

An Improved Random Access Scheme Using Directional Beams for 5G Massive Machine-Type Communications

In the 5th generation (5G) massive machine-type communication (mMTC), random access is the limiting factor of performance because there may be massive user equipments (UEs) up to 1 million unit competing for the access opportunities. Existing random access schemes in the 4th generation (4G) or 5G systems are not able to handle such large amount of concurrent random access requests. This paper proposes a random access scheme based on directional beams, which offers a new spatial degree of freedom to improve the random access performance in 5G mMTC. In this scheme, a cell is divided into beam zones in space domain. UEs in different beam zones choose a certain physical random access channel (PRACH) resource with different probability. When preamble collision occurs, uplink radio resource is allocated to the beam zone which has low collision probability. We provide theoretical performance analysis of the proposed scheme using different performance metrics. Comparisons are carried out between the proposed and several existing random access schemes. The proposed scheme can be easily deployed without any modification to 5G framework. Existing random access optimizing algorithms can also be applied to the proposed scheme.

Dynamic Analysis of Alternative Elements in an Automated Packaging System Based on 5G Internet of Things

The arrival of 5G will usher in an era of “Internet of Everything.” Massive Internet of Things data contains great value in the dynamic analysis of alternative elements of automated packaging systems. From the perspective of the realization of personalized customization functions, this article solves the problem of dynamic analysis of alternative elements in the automated packaging system. We analyze the connection mechanism and interaction method between the cloud service system layer and the mobile terminal service layer, and carry out the corresponding software design. From the perspective of the realization of the intelligent production of the system in this paper, this topic mainly studies the information interaction mechanism and production control mechanism of the cloud service system and the manufacturing system. Based on the hardware of the manufacturing system layer, a flexible production implementation mechanism is formulated to make it the basis for the implementation of intelligent production of the system. Based on the massive data processing capabilities of the cloud service system, the information processing mechanism and the production planning decision-making mechanism are formulated for it, so as to realize the intelligent adjustment of the manufacturing system layer in the production process. For the connection scenario of IoT group paging, based on the application of NB-IoT technology in the next-generation mobile communication network, the focus of network optimization is to ensure the random access performance of IoT devices as much as possible. To this end, this paper proposes a random access optimization strategy for IoT group paging based on time slot scattering. We establish a mathematical model based on queuing theory for the connection scenario of the Io T group paging, then use the mathematical formula to derive the number of IoT devices scattered to each time slot in the initial state, thereby deriving the specific time slot scattering algorithm. This paper establishes a list of credit nodes, changes the participation mode of consensus nodes from static to dynamic, and supports voting to select trusted nodes. We designed a credit evaluation mechanism as a basis for consensus node elections to improve system’s fault tolerance rate. The algorithm process was simplified, and the PBFT algorithm process was simplified from a 3-phase protocol to a 2-phase protocol to further reduce communication bandwidth overhead and algorithm time. Simulation analysis shows that, compared with the PBFT algorithm, the proposed algorithm improves node flexibility and fault tolerance while reducing communication bandwidth overhead by about 45%, packaging throughput by about 4%, and latency by about 3%.

Performance Analysis of Signature-Based Grant-Free Random Access Under Impersonation Attacks

In massive machine-type communications (mMTC), user-specific and non-orthogonal signatures can be assigned to wireless devices for uplink grant-free access. In this paper, we analyze the performance of signature-based grant-free random access in the presence of an adversary. To enhance the access security, we assume that an mMTC system renews the signatures of legitimate devices at each access secretly between the devices and a base station (BS). To gain unauthorized access to this mMTC system, we assume that the adversary attempts to impersonate legitimate devices by sending malicious signatures to the BS. By this impersonation attack, the adversary takes a chance to modify the data transmitted from legitimate devices and/or inject false data to the system. Moreover, this attack may deteriorate the performance of activity detection of legitimate devices through jamming. Under this scenario, we investigate the impacts of this impersonation attack on the performance of signature-based grant-free access theoretically and numerically, with respect to data modification, false authentication, and jamming.

Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches

Machine learning (ML) has gained attention from the network research community because it can help solve difficult problems and potentially lead to groundbreaking achievements. In the Wi-Fi domain, ML is applied to solve challenges such as efficient channel access and fair coexistence with other technologies in unlicensed bands. In this paper, we address the performance of uplink orthogonal frequency division multiple random access (UORA) in IEEE 802.11ax networks. Optimization of UORA is a good case for applying ML because of its inherent complexity and dependence on situation and time-dependent parameters. In particular, we use deep reinforcement learning to tune UORA parameters. Our simulation results show that even though the ML-based solution leads to close to optimal results, its operation is comparable to a much simpler, non-ML heuristic. Therefore, we conclude that ML-based solutions to improve IEEE 802.11 performance need not exceed well-designed heuristics.

Pattern Division Random Access (PDRA) for M2M Communications With Massive MIMO Systems

In this work, we introduce the pattern-domain pilot design paradigm based on a superposition of orthogonal-building-blocks with significantly larger contention space to enhance the massive machine-type communications (mMTC) random access (RA) performance in massive multiple-input multiple-output (MIMO) systems. Specifically, the pattern-domain pilot is constructed based on the superposition of L cyclically-shifted Zadoff-Chu (ZC) sequences. The pattern-domain pilots exhibit zero correlation values between non-colliding patterns from the same root and low correlation values between patterns from different roots. The increased contention space, i.e., from N to C_N^L, where C_N^L denotes the number of all L-combinations of a set N, and low correlation values lead to a significantly lower pilot collision probability without compromising excessively on channel estimation performance for mMTC RA in massive MIMO systems. We present the framework and analysis of the RA success probability of the pattern-domain based scheme with massive MIMO systems. Numerical results demonstrate that the proposed pattern division random access (PDRA) scheme achieves an appreciable performance gain over the conventional one, while preserving the existing physical layer virtually unchanged. The extension of the superposition of orthogonal-building-blocks scheme to superposition of quasi-orthogonal-building- blocks is straightforward.

Multi-Sequence Spreading Random Access (MSRA) for Compressive Sensing-Based Grant-Free Communication

The performance of grant-free random access (GF-RA) is limited by the number of accessible random access resources (RRs) due to the absence of collision resolution. Compressive sensing (CS)-based RA schemes scale up the RRs at the expense of increased non-orthogonality among transmitted signals. This paper presents the design of multi-sequence spreading random access (MSRA) which employs multiple spreading sequences to spread the different symbols of a user as opposed to the conventional schemes in which a user employs the same spreading sequence for each symbol. We show that MSRA provides code diversity, enabling the multi-user detection (MUD) to be modeled into a well-conditioned multiple measurement vectors (MMVs) CS problem. The code diversity is quantified by the decrease in the average Babel mutual coherence among the spreading sequences. Moreover, we present a two-stage active user detection (AUD) scheme for both wideband and narrowband implementations. Our theoretical analysis shows that with MSRA activity misdetection falls exponentially while the size of GF-RA frame is increased. Finally, the simulation results show that about 82% increase in utilization of RRs, i.e., more active users, is supported by MSRA than the conventional schemes while achieving the RA failure rate lower bound set by random access collision.

Throughput Analysis and User Barring Design for Uplink NOMA-Enabled Random Access

Being able to accommodate multiple simultaneous transmissions on a single channel, non-orthogonal multiple access (NOMA) appears as an attractive solution to support massive machine type communication (mMTC) that faces a massive number of devices competing to access the limited number of shared radio resources. In this paper, we first analytically study the throughput performance of NOMA-based random access (RA), namely NOMA-RA. We show that while increasing the number of power levels in NOMA-RA leads to a further gain in maximum throughput, the growth of throughput gain is slower than linear. This is due to the higher-power dominance characteristic in power-domain NOMA known in the literature. We explicitly quantify the throughput gain for the very first time in this paper. With our analytical model, we verify the performance advantage of NOMA-RA scheme by comparing with the baseline multi-channel slotted ALOHA (MS-ALOHA), with and without capture effect. Despite the higher-power dominance effect, the maximum throughput of NOMA-RA with four power levels achieves over three times that of the MS-ALOHA. However, our analytical results also reveal the sensitivity of load on the throughput of NOMA-RA. To cope with the potential bursty traffic in mMTC scenarios, we propose adaptive load regulation through a practical user barring algorithm. By estimating the current load based on the observable channel feedback, the algorithm adaptively controls user access to maintain the optimal loading of channels to achieve maximum throughput. When the proposed user barring algorithm is applied, simulations demonstrate that the instantaneous throughput of NOMA-RA always remains close to the maximum throughput confirming the effectiveness of our load regulation.

Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.

On Throughput Improvement Using Immediate Re-Transmission in Grant-Free Random Access With Massive MIMO

To support machine-type communication (MTC), massive multiple-input multiple-output (MIMO) has been considered for grant-free random access. In general, the performance of grant-free random access with massive MIMO is limited by the number of preambles and the number of active devices. In particular, when there are a number of active devices transmitting data packets simultaneously, the signal-to-interference-plus-noise ratio (SINR) cannot be high enough for successful decoding. In this paper, in order to improve performance, we consider immediate re-transmissions for an active device that has a low SINR although it does not experience preamble collision to exploit re-transmission diversity (RTD) gain. To see the performance of the proposed approach, we perform throughput analysis with certain approximations and assumption. Since the proposed approach can be unstable due to immediate re-transmissions, conditions for stable systems are also studied. Simulations are carried out and it is shown that analysis results reasonably match simulation results.

Throughput of CDM-Based Random Access With SINR Capture

Code division multiplexing (CDM)-based random access, wherein a set of sequences is reserved for random access and a remote station transmits a random access packet using a sequence randomly selected from this set, is employed in numerous practical wireless systems. If multiple remote stations simultaneously transmit random access packets using the same sequence, the performance degrades owing to sequence collision. In addition, the performance is degraded because of interference if multiple remote stations simultaneously transmit random access packets using different sequences. Therefore, the performance of CDM-based random access is dependent on both sequence collision and interference. There have been no previous studies on analyzing the performance of CDM-based random access considering both sequence collision and interference. In this paper, the throughput of CDM-based random access is investigated considering both sequence collision and interference based on a signal-to-interference-plus-noise ratio (SINR) capture model. Analyses and numerical simulations are performed to compare the throughputs of several random access schemes, including conventional and channel-adaptive random access. The results show that channel-adaptive random access can achieve significantly higher throughput than that achieved by conventional random access. Moreover, the results demonstrate that it is possible to analyze the trade-off between the throughput and implementation complexity with the increased the number of sequences.

Random Access and Detection Performance of Internet of Things for Smart Ocean

Over the last decade, the Internet of Things (IoT) has been employed as an enabling technology for the smart ocean. As one of the key technologies in the IoT, machine-type communication (MTC) has been considered to support devices’ connectivity. In the MTC, random access is introduced for devices to share a common access channel during the packet transmission with low signaling overhead. However, the collision caused by the presence of multiple devices is inevitable. Since maritime sensors have limited energy sources, in this article, we propose a relay-aided random access (RARA) scheme for the smart ocean, where retransmissions are carried out by maritime buoys with the relay function, to deal with collisions. In the RARA scheme, a base station (BS) is able to recover multiple collided signal packets simultaneously by using multiuser detection with multiple copies of collided signals forwarded by buoy nodes. As a result, our proposed scheme becomes energy efficient and reliable to be suitable for the smart ocean. Theoretical and simulation results show that a high throughput and a low outage probability can be achieved with a large number of buoy nodes.

Spatial Group Based Optimal Uplink Power Control for Random Access in Satellite Networks

In this paper, we propose a spatial group based optimal uplink power control scheme to enhance the performance of random access (RA) in satellite networks. In the proposed scheme, satellite machine-type terminals (SMTs) are divided into multiple groups based on their spatial locations in a distributed fashion, and the target received power levels of SMTs are the same in the same group but different from group to group by means of uplink power control, which enables the received power diversity. The received power levels of SMTs in different groups are designed by taking into account their path losses, which effectively reduces the energy consumption of SMTs. The threshold based capture model at the gateway demodulator is adopted, and the packet loss ratio (PLR) based on this model is derived by using density evolution analysis. An optimization problem is formulated and solved to jointly find the optimal number of power levels and the optimal values of power levels, which maximizes the RA system throughput. Simulation results show that the proposed scheme can obtain considerable performance improvement with respect to the conventional schemes in terms of normalized throughput, PLR, and average energy consumption.

Processing Grid-format Real-world Graphs on DRAM-based FPGA Accelerators with Application-specific Caching Mechanisms

Graph processing is one of the important research topics in the big-data era. To build a general framework for graph processing by using a DRAM-based FPGA board with deep memory hierarchy, one of the reasonable methods is to partition a given big graph into multiple small subgraphs, represent the graph with a two-dimensional grid, and then process the subgraphs one after another to divide and conquer the whole problem. Such a method (grid-graph processing) stores the graph data in the off-chip memory devices (e.g., on-board or host DRAM) that have large storage capacities but relatively small bandwidths, and processes individual small subgraphs one after another by using the on-chip memory devices (e.g., FFs, BRAM, and URAM) that have small storage capacities but superior random access performances. However, directly exchanging graph (vertex and edge) data between the processing units in FPGA chip with slow off-chip DRAMs during grid-graph processing leads to limited performances and excessive data transmission amounts between the FPGA chip and off-chip memory devices. In this article, we show that it is effective in improving the performance of grid-graph processing on DRAM-based FPGA hardware accelerators by leveraging the flexibility and programmability of FPGAs to build application-specific caching mechanisms, which bridge the performance gaps between on-chip and off-chip memory devices, and reduce the data transmission amounts by exploiting the localities on data accessing. We design two application-specific caching mechanisms (i.e., vertex caching and edge caching ) to exploit two types of localities (i.e., vertex locality and subgraph locality ) that exist in grid-graph processing, respectively. Experimental results show that with the vertex caching mechanism, our system (named as FabGraph) achieves up to 3.1× and 2.5× speedups for BFS and PageRank, respectively, over ForeGraph when processing medium graphs stored in the on-board DRAM. With the edge caching mechanism, the extension of FabGraph (named as FabGraph+) achieves up to 9.96× speedups for BFS over FPGP when processing large graphs stored in the host DRAM.