Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach
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Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management. Exploiting the bursting nature of the access requests, sparse active user detection (SAUD) is an efficient enabler towards efficient access management, but the sparsity might be deteriorated in case of uncoordinated massive access requests. To dynamically preserve the sparsity of access requests, a reinforcement-learning (RL)-assisted scheme of closed-loop access control utilizing the access class barring technique is proposed, where the RL policy is determined through continuous interaction between the RL agent, i.e., a next generation node base (gNB), and the environment. The proposed scheme can be implemented by the near-real-time RAN intelligent controller (near-RT RIC) in O-RAN, supporting rapid switching between heterogeneous vertical applications, such as mMTC and uRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces, where a replay buffer is applied for automatic large-scale data collection. An actor-critic framework is formulated to incorporate the strategy-learning modules into the near-RT RIC. Simulation results show that the proposed schemes can achieve superior performance in both access efficiency and user detection accuracy over the benchmark scheme for different heterogeneous services with massive access requests.Keywords:
Benchmark (surveying)
Random access
The random access methods used for support of machine-type communications (MTC) in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. Motivated by the random access method employed in LTE, we propose a novel approach that is able to sustain a wide random access load range, while preserving the physical layer unchanged and incurring minor changes in the medium access control layer. The proposed scheme increases the amount of available contention resources, without resorting to the increase of system resources, such as contention sub-frames and preambles. This increase is accomplished by expanding the contention space to the code domain, through the creation of random access codewords. Specifically, in the proposed scheme, users perform random access by transmitting one or none of the available LTE orthogonal preambles in multiple random access sub-frames, thus creating access codewords that are used for contention. In this way, for the same number of random access sub-frames and orthogonal preambles, the amount of available contention resources is drastically increased, enabling the support of an increased number of MTC users. We present the framework and analysis of the proposed code-expanded random access method and show that our approach supports load regions that are beyond the reach of current systems.
Random access
Aloha
Code (set theory)
Access method
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Random access
LTE Advanced
Access network
Ran
C-RAN
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Densification of mobile networks leads to some problems such as overloaded backhaul and increased latency. Base stations may not always have fiber backhauls. For such cases, radio links are employed between base stations towards a base station that has a high capacity backhaul link. Those set of base stations that are connected to the hub over radio links are called radio clusters. We propose caching in radio clusters for decreasing the video streaming load on radio links. We define an optimization problem to determine the cache locations for video-on-demand segments considering user requirements. We tested our model with different radio cluster configurations concentrating on the size of the radio cluster, the number of users, and the number of demanded videos. Significant gains can be attained by employing the proposed cache placement and replication solution in radio clusters.
Backhaul (telecommunications)
Radio Link Protocol
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The random access methods used for support of machine-type communications (MTC) in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. Motivated by the random access method employed in LTE, we propose a novel approach that is able to sustain a wide random access load range, while preserving the physical layer unchanged and incurring minor changes in the medium access control layer. The proposed scheme increases the amount of available contention resources, without resorting to the increase of system resources, such as contention sub-frames and preambles. This increase is accomplished by expanding the contention space to the code domain, through the creation of random access codewords. Specifically, in the proposed scheme, users perform random access by transmitting one or none of the available LTE orthogonal preambles in multiple random access sub-frames, thus creating access codewords that are used for contention. In this way, for the same number of random access sub-frames and orthogonal preambles, the amount of available contention resources is drastically increased, enabling the support of an increased number of MTC users. We present the framework and analysis of the proposed code-expanded random access method and show that our approach supports load regions that are beyond the reach of current systems.
Random access
Aloha
Code (set theory)
Access method
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Learning a high-performance trade execution model via reinforcement learning (RL) requires interaction with the real dynamic market. However, the massive interactions required by direct RL would result in a significant training overhead. In this paper, we propose a cost-efficient reinforcement learning (RL) approach called Deep Dyna-Double Q-learning (D3Q), which integrates deep reinforcement learning and planning to reduce the training overhead while improving the trading performance. Specifically, D3Q includes a learnable market environment model, which approximates the market impact using real market experience, to enhance policy learning via the learned environment. Meanwhile, we propose a novel state-balanced exploration scheme to solve the exploration bias caused by the non-increasing residual inventory during the trade execution to accelerate model learning. As demonstrated by our extensive experiments, the proposed D3Q framework significantly increases sample efficiency and outperforms state-of-the-art methods on average trading cost as well.
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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
Preamble
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The expected tremendous growth of machine-to-machine (M2M) devices will require solutions to improve random access channel (RACH) performance. Recent studies have shown that radio access network (RAN) performance is degraded under the high density of devices. In this paper, we propose three methods to enhance RAN performance for M2M communications over the LTE-A standard. The first method employs a different value for the physical RACH configuration index to increase random access opportunities. The second method addresses a heterogeneous network by using a number of picocells to increase resources and offload control traffic from the macro base station. The third method involves aggregation points and addresses their effect on RAN performance. Based on evaluation results, our methods improved RACH performance in terms of the access success probability and average access delay.
Random access
Machine to machine
LTE Advanced
Macro
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For low latency communications, we propose an enhanced random access scheme that avoids random access retrials from the beginning of the random access due to the expiry of a random access response window. The proposed random access scheme exploits multiple transmissions of random access preambles and random access responses. We also provide model and analysis of random access latency with expected delay and the probability of preamble collision. Our numerical results show that the proposed scheme provides lower latency.
Random access
Preamble
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Supporting a large number of devices in LTE Machine-to-Machine (M2M) communications is a big challenge. The large number of devices will try to access radio resource in a short period of time which may result in severe congestion to the Radio Access Network (RAN). Hence, how to scatter the devices, which are always trying to access to a same eNBs (evolved Node B), to other eNBs has become an important issue. In other words, balancing the random access intensity among eNBs will reduce the congestion of an RAN and improve the network utilization. Nevertheless, how to select an eNB to attach to during the initial access in a way that will balance overall access intensity in the network is a key challenge. Unfortunately, from the perspective of eNB, an eNB is unaware of how many devices will access to the eNB. Thus, an eNB cannot release the information about the access intensity in advance. In this paper, therefore, we propose four effective eNB selection algorithms to estimate the access intensity of an eNB. Simulation results show that the proposed algorithms perform better than conventional approach.
Random access
Access network
Traffic intensity
LTE Advanced
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In this paper, we study the capacity (i.e., the maximum achievable throughput) of radio access networks that exploit mobile small cell base stations carried by vehicles for adaptive densification in urban areas. While traditional approaches for radio access network densification with fixed small cell base stations are proving ineffective and extremely costly, mobile small cell base stations carried by vehicles can provide adaptive densification while achieving higher efficiency and lower cost. As a matter of fact, the existence of correlations between the number of mobile network subscribers and the number of vehicles in a given area allows for the spontaneous creation of temporary dense small cell deployments where and when needed. Ultimately, this approach to Radio Access Network densification increases efficiency, hence reducing costs for the operator. In this context, we first present an approach for the computation of the maximum throughput that can be obtained in an area served by traditional fixed base stations and mobile small cell base stations. We then provide initial estimates for the throughput improvements with respect to traditional deployments that rely on fixed base stations only. Evaluations in the realistic case study of the main railway station area in Milan, Italy, reveal that the use of mobile base stations achieves throughout gains up to 120% over legacy fixed access infrastructures, while granting higher fairness among subscribers.
Small cell
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