Reduce Signaling Overhead Research Articles

Optimization of Mixed Numerology Profiles for 5G Wireless Communication Scenarios

The management of 5G resources is a demanding task, requiring proper planning of operating numerology indexes and spectrum allocation according to current traffic needs. In addition, any reconfigurations to adapt to the current traffic pattern should be minimized to reduce signaling overhead. In this article, the pre-planning of numerology profiles is proposed to address this problem, and a mathematical optimization model for their planning is developed. The idea is to explore requirements and impairments usually present in a given wireless communication scenario to build numerology profiles and then adopt one of the profiles according to the current users/traffic pattern. The model allows the optimization of mixed numerologies in future 5G systems under any wireless communication scenario, with specific service requirements and impairments, and under any traffic scenario. Results show that, depending on the granularity of the profiles, the proposed optimization model is able to provide satisfaction levels of 60–100%, whereas a non-optimized approach provides 40–65%, while minimizing the total number of numerology indexes in operation.

Joint User Identification and Channel Estimation via Exploiting Spatial Channel Covariance in mMTC

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.

Performance Analysis of Grant-Free Random-Access NOMA in URLL IoT Networks

Internet-of-Things (IoT) networks have recently emerged to provide massive connectivity for many application scenarios and services. Additionally, developing spectrum-access strategies for a large number of nodes with sporadic data traffic behaviors in IoT networks has attracted much attention recently. However, developing such strategies becomes more challenging when ultra-reliable low-latency (URLL) transmissions are required. As IoT networks entail spectrum-efficient transmission schemes, non-orthogonal multiple-access (NOMA) has emerged as a key enabler for such networks. On the other hand, grant-free random-access (RA) techniques are particularly promising for high spectral-efficiency and massive connectivity, since they reduce signaling overhead, and packet latency. Therefore, in this paper, uplink RA-NOMA IoT networks with clustered IoT devices is studied, where short packet and diversity transmissions are adopted to meet the URLL requirements. To reduce the negative effect of diversity transmission on packet latency, multiple replicas of packets are accommodated within different resource blocks (RBs) in the same transmission time interval (TTI). The analytical expressions of network metrics, namely, average packet latency, reliability, and GoodPut are derived. Furthermore, the effect of the number of packet replicas, blocklength, and cluster size on the network metrics is evaluated. Finally, the analytical derivations are utilized to find the optimal values for the number of packet replicas, blocklength, and power control parameters, such that the network GoodPut is maximized, subject to URLL constraints.

Signaling Overhead Reduction Techniques in Device-to-Device Communications: Paradigm for 5G and Beyond

Device-to-Device (D2D) communications have recently attracted researchers, attention because of their numerous applications in industry verticals. It enables communications among devices without or with the partial involvement of a central system. To initiate a D2D communications device discovery and radio resource allocation is a critical task when devices have high mobility. Maintaining the quality-of-service and continuous connectivity requires a signaling burden. An efficient mobility management procedure is necessary to discover the neighboring devices in D2D communications systems. The Discovery of a massive number of devices requires an effective radio resource management procedure that causes signaling overhead. In 5G and beyond communication system, two mobility management methods exist; device discovery and beaconing. Since device density and traffic increases exponentially with high mobility, hence device discovery and beaconing increase the signaling overhead and energy consumption in power-limited devices. Thus, signaling overhead research needs much attention in 5G and beyond systems to meet the service requirements like accuracy, latency, and battery life. Therefore, the challenges and the techniques related to signaling overhead in D2D communications are presented.

Accumulated Polar Feature-Based Deep Learning for Efficient and Lightweight Automatic Modulation Classification With Channel Compensation Mechanism

In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role in future intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, conventional DL-based approaches suffer from heavy training overhead, memory overhead, and computational complexity, which severely hinder practical applications for resource-limited scenarios, such as Vehicle-to-Everything (V2X) applications. Furthermore, the overhead of online retraining under time-varying fading channels has not been studied in the prior arts. In this work, an accumulated polar feature-based DL with a channel compensation mechanism is proposed to cope with the aforementioned issues. Firstly, the simulation results show that learning features from the polar domain with historical data information can approach near-optimal performance while reducing training overhead by 99.8 times. Secondly, the proposed neural network-based channel estimator (NN-CE) can learn the channel response and compensate for the distorted channel with 13% improvement. Moreover, in applying this lightweight NN-CE in a time-varying fading channel, two efficient mechanisms of online retraining are proposed, which can reduce transmission overhead and retraining overhead by 90% and 76%, respectively. Finally, the performance of the proposed approach is evaluated and compared with prior arts on a public dataset to demonstrate its great efficiency and lightness.

A bandwidth efficient HMAC-based authentication scheme for network coding

Pollution attacks in network coding result in the waste of bandwidth and computational resources. Several homomorphic message authentication code (HMAC) and signature-based schemes have been proposed to mitigate such attacks. In these schemes, authentication is achieved by appending several HMAC tags with or without a signature to the packet payload. This approach negatively affects the throughput as well as the communication overhead incurred by such schemes. A phenomenon determined by a property we dubbed as the admissible payload rate (APR). In this paper we propose an HMAC and signature-based authentication scheme that addresses the afore mentioned problems. It achieves this by reducing the number of symbols needed to represent a packet. We evaluated the performance of our scheme and compared our findings to the performance of similar, state of the art authentication schemes. Our comparisons revealed that the proposed scheme incurred a negligible percentage gain in computational complexity. However, with no extra key storage overhead, and without sacrificing security, our scheme achieved a significant percentage reduction in communication overhead. The above mentioned favorable characteristics make our scheme an ideal and practical authentication solution for networks where bandwidth is a constraint or an optimal throughput is a requirement.

Revolving Codes: Overhead and Computational Complexity Analysis

Revolving codes (ReC) are a new family of network codes that reduce signaling overhead and maintain high decoding probability, which is key in applications with small payloads, e.g., IoT, Industry 4.0. However, they have only been studied using simulations. We present i) the first exact mathematical model for the total overhead and decoding probability of a set of ReCs, and ii) efficient encoders and decoders. Our analysis shows that ReC decoders' number of multiplications scales as O(g) where g is the number of data packets being combined. In practice, this translates in several orders of magnitude fewer multiplications, half the number of additions, and lower transmission overheads compared to RLNC.

A Framework for MEC-Enhanced Small-Cell HetNet with Massive MIMO

In this article, we propose a framework for a multi-access edge computing-enhanced heterogeneous network employing massive MIMO transmission and small cells that is designed to mitigate interference, increase throughput, alleviate backhaul capacity pressure, and reduce signaling overhead. We design the information-centric transmission mechanism and discuss possible signal processing and transmission modes. We also demonstrate that channel state information can be pooled at a centralized MEC server, and precoding may be carried out cooperatively among cells to reduce intra-cell and inter-cell interference. A smart caching scheme is proposed to proactively cache content at appropriate base stations in anticipation of the best transmission mode to be used, thereby reducing latency from content retrieval. Moreover, our proposed scheme performs content scheduling to assign the same content requested by different users to the same intervals, eliminating some possible interference and simplifying precoding. Finally, research challenges within the proposed framework are discussed.

Deep Reinforcement Learning for Throughput Improvement of the Uplink Grant-Free NOMA System

Facing the dramatic increase of mobile devices and the scarcity of spectrum resources, grant-free nonorthogonal multiple access (NOMA) emerges as an enabling technology for massive access, which also reduces signaling overhead and access latency effectively. However, in grant-free NOMA systems, the collisions resulting from uncoordinated resource selection can cause severe interference and reduce system throughput. In this article, we apply deep reinforcement learning (DRL) in the decision making for grant-free NOMA systems, to mitigate collisions and improve the system throughput in an unknown network environment. To reduce collisions in the frequency domain and the computational complexity of DRL, subchannel and device clustering are first designed, where a cluster of devices compete for a cluster of subchannels following grant-free NOMA. Furthermore, discrete uplink power control is proposed to reduce intracluster collisions. Then, the long-term cluster throughput maximization problem is formulated as a partially observable Markov decision process (POMDP). To address the POMDP, a DRL-based grant-free NOMA algorithm is proposed to learn about the network contention status and output subchannel and received power-level selection with less collisions. The numerical results verify the effectiveness of the proposed algorithm and reveal that DRL-based grant-free NOMA outperforms slotted ALOHA NOMA with 32.9% and 156% performance gain on the system throughput when the number of devices is twice and five times that of the subchannels, respectively. When the number of devices is five times that of the subchannels, the success access probability of DRL-based grant-free NOMA is above 85%, compared to 33% in the slotted ALOHA NOMA system.

Group-Based Data Transmission Protocol for Small-Sized URLLC Services

This letter presents a group-based data transmission protocol for small-sized ultra-reliable and low latency communication (URLLC) services. For the users within the same group, the semi-persistent scheduling (SPS) and grant-free based scheduling schemes are adopted for their initial transmission and retransmission to reduce signaling overhead consumption, respectively. We further investigate the optimal number of consecutive slots within one group and derive a closed-form optimal configuration to maximize the utilization of retransmission resources. Results show that the proposed protocol can significantly reduce the control signaling while guaranteeing the stringent latency and reliability requirements.

Prediction-Based Conditional Handover for 5G mm-Wave Networks: A Deep-Learning Approach

Conditional handover (CHO) is one of several promising mobility enhancements in 5G networks. By making preparation decisions earlier than in LTE HO, CHO can provide an improved HO success rate. This article, analyzes the strengths and weaknesses of CHO by comparing CHO to 5G baseline HO. Since millimeter-wave communications are vulnerable to blockages, sudden changes in signal reception power can mislead CHO into making undesired early preparations in 5G networks. To enhance the robustness of CHO, current studies propose using an increased number of preparations, resulting in considerable signaling overhead. This article offers a novel prediction-based CHO (PCHO) scheme that uses deep-learning technology to overcome the weaknesses of CHO and make more intelligent preparation decisions. Based on the changes in the signal patterns of the base stations, PCHO uses former blockage information to predict the best next base station to which to conduct HO. Performance evaluation demonstrates that PCHO can improve the early preparation success rate while reducing signaling overhead compared to current CHO schemes.

Hybrid beamforming design based on unsupervised machine learning for millimeter wave systems

SummaryThis article proposes a hybrid beamforming design with reduced channel state information (CSI) feedback. We use a beam sweeping procedure to provide channel measurements and feed a CSI report scheme. Thereby, the base station (BS) can perform an adequate estimation of the channel characteristics with reduced signaling overhead. Consequently, we need short pilot sequences and very few precoding matrix indicators (PMIs) to properly describe channel behavior. Moreover, we also evaluate different user selection strategies based on unsupervised machine learning framework that exploits the channel information provided by the proposed beam sweeping scheme. Our performance evaluation indicates that the user selection based on fuzzy c‐means is able to efficiently explore the reduced CSI. The proposed hybrid beamforming scheme reduces the multi‐user interference and achieves significant gains in total data rate as channel conditions and interference environment becomes more challenging.

A Distributed Survivable Routing Algorithm for Mega-Constellations With Inclined Orbits

Mega-constellations consisting of hundreds to thousands of low-earth-orbit (LEO) satellites are an attractive solution for providing global ubiquitous network access. Due to good coverage properties for populated areas, inclined orbits are gaining popularity among commercial constellations. A scalable routing algorithm with survivability plays a key role in such systems. In this paper, we propose a distributed survivable routing algorithm for mega-constellations with inclined orbits. First, the special topology characteristic of inclined constellations is identified and formalized. Based on the topology characterization, a basic X-Y routing algorithm is presented to determine multiple primary and secondary paths towards each destination utilizing the regularity of the network topology with minimal computation overhead. Then, a failure recovery mechanism which consists of a restricted flooding mechanism and a pre-detour mechanism is proposed to reduce end-to-end delay and signaling overhead in case of link failures. Besides, a partial-record loop avoidance mechanism is proposed to deal with routing loops with minimal overhead. Finally, a vector-based next hop selection mechanism is proposed to facilitate the selection of next hop while incorporating various criteria. The performance of the proposed routing algorithm is evaluated through simulation on the Starlink constellation. Simulation results show that our proposal achieves scalability by reducing signaling overhead and provides better quality of service in terms of end-to-end delay under link failures.

Random-Access NOMA in URLL Energy-Harvesting IoT Networks With Short Packet and Diversity Transmissions

Non-orthogonal multiple-access (NOMA) has recently been proposed to improve throughput and spectrum-efficiency of 5G cellular networks and beyond. It is also a key enabler for ultra-reliable and low-latency (URLL) communications. Moreover, the Internet-of-Things (IoT) paradigm has emerged to practically provide massive connectivity for smart devices and systems, which entails spectrum- and energy-efficient transmission schemes. To this aim, NOMA and energy-harvesting (EH) solutions have been put forth to address such demands, making the combination of such technologies inevitable in NOMA-based URLL-EH-IoT networks. On the other hand, random-access (RA) techniques are the key solution to enable massive URLL-EH-IoT networks, since they reduce signaling overhead, packet latency, and energy consumption when massive numbers of clustered IoT devices with sporadic traffic behavior are considered. In this paper, uplink RA-NOMA in URLL-EH-IoT networks with short packet and diversity transmissions is studied and analyzed. Network metrics—such as average packet latency, reliability, and GoodPut—are derived for the RA-NOMA scenario and compared with its RA-OMA counterpart to explore the advantages of RA-NOMA over RA-OMA. Moreover, the analytical derivations have been validated and shown to coincide with the simulation results. Furthermore, the effect of the transmission diversity and number of data bits per blocklength on the different network metrics are extensively evaluated. Lastly, the analytical derivations are utilized to find the optimum values of the IoT nodes’ transmit power, number of packet replicas, and number of transmitted data bits per blocklength, such that the maximum sum-GoodPut of a RA-NOMA IoT cluster is achieved, subject to URLL constraints.

STC: Sub-packetization tunable codes for fast recovery

Abstract In distributed cyber-physical systems, the data should be stored in a reliable and available manner in the face of multiple kinds of failures. Considering that the storage space is an expensive part, erasure codes are storage-efficient alternatives to replication for fault tolerance. However, traditional erasure codes, e.g. Reed–Solomon (RS) codes, impose a huge burden on disk I/O and network resources for recovering unavailable data in case of node failures. In this paper, we introduce a new set of erasure codes called Sub-packetization Tunable Codes (STC). STC can reduce the amount of required information during recovering a failed block, while still allowing a significant reduction in storage overhead. The customized recovery method in STC needs each block to be sub-packetized, and the repair bandwidth decreases as the number of sub-packets increases. However, high sub-packetization may result in some extra costs. In STC, the sub-packetization level of our codes is tunable so that storage systems can weigh the balance between sub-packetization costs and repair bandwidth. In addition, our STC can be constructed as a family of double regenerating codes (DRC) to improve the repair performance in hierarchical distributed situations, whose idea is to trade the abundant inner-rack bandwidth for constrained cross-rack bandwidth.

Magic State Distillation: Not as Costly as You Think

Despite significant overhead reductions since its first proposal, magic state distillation is often considered to be a very costly procedure that dominates the resource cost of fault-tolerant quantum computers. The goal of this work is to demonstrate that this is not true. By writing distillation circuits in a form that separates qubits that are capable of error detection from those that are not, most logical qubits used for distillation can be encoded at a very low code distance. This significantly reduces the space-time cost of distillation, as well as the number of qubits. In extreme cases, it can cost less to distill a magic state than to perform a logical Clifford gate on full-distance logical qubits.

Routing Algorithm To Eliminate Stale Routes (RAESR) In Mobile Ad Hoc Networks

Mobile Ad-hoc Network (MANET) is a collection of self sustaining mobile nodes which are connected through many wi-fi links to form a temporary communication for sharing information between the users. Mobile nodes behave as a host as well as router. As nodes in MANET posse’s mobility in traits frequently leads to irregular link between the nodes. Link failure directs a significant routing overhead during high mobility and also maintaining all the information associated with nodes and routing paths are considered as an extra overhead on the table. In order to overcome these issues, the routing algorithm to eliminate stare routed in routing cache. The neighbor degree centrality table is introduced to recognize the valuable nodes, using the valuable nodes the routes are discovered and link failure information are disseminated across the network wide. The results and findings show that the elimination of stale routes leads to significant reduction in routing overhead which in turn reduces the route error propagation delay.

Lightweight (Reverse) Fuzzy Extractor With Multiple Reference PUF Responses

A physical unclonable function (PUF), like a fingerprint, exploits manufacturing randomness to endow each physical item with a unique identifier. One primary PUF application is the secure derivation of volatile cryptographic keys using a fuzzy extractor (FE) comprising: 1) a secure sketch and 2) an entropy extractor. Although the entropy extractor can be lightweight, the overhead of the secure sketch responsible for correcting naturally noisy PUF responses is usually high. We observe that, in general, response unreliability with respect to an enrolled reference measurement increases with increasing differences between the in-the-field PUF operating condition and the operating condition used in evaluating the enrolled reference response. For the first time, we exploit such an inadvertent but important observation. In contrast to the conventional single reference response enrollment, we propose enrolling multiple reference responses (MRRs) subject to the same challenge but under multiple distinct operating conditions. The critical observation here is that one of the reference operating conditions is likely to be closer to the operating condition of the field deployed PUF, thus resulting in minimizing the expected unreliability when compared to the single reference under the nominal condition. As a consequence, MRR greatly reduces the demand for the expected number of erroneous bits requiring correction and, subsequently, achieves a significant reduction in the error correction overhead. The significant implementation efficiency gains from the proposed MRR method are demonstrated from software implementations of FEs on batteryless resource constraint computational radio frequency identification devices, where realistic PUF data are collected from intrinsic static random access memory PUFs.

Joint Interference Cancellation and Resource Allocation for Full-Duplex Cloud Radio Access Networks

In this paper, we study joint interference cancellation and resource allocation for full-duplex (FD) cloud radio access networks (C-RANs) with antenna correlation. The target is to maximize the capacity of downlink (DL) user equipments (UEs) with guaranteed quality of service (QoS) of uplink (UL) UEs under the constraints of DL and UL transmit power. Intractability of the considered problem involves non-convexity and coupling between postcoding and remote radio head (RRH) selection. To deal with the high coupling between system variables, an estimation-free self-interference (SI) cancellation (EFSC) scheme with merit of significantly reducing signaling overhead for channel estimation is proposed. The reduced signaling overhead is also derived in closed-form expression. Furthermore, we propose a generalized Bender’s decomposition-based resource allocation (GRA) algorithm, which separates the continuous and discrete variables to solve the optimization problem. With the design of a flexible utility function, the tradeoff between DL capacity and co-channel interference (CCI) can be achieved. Moreover, we identify the scenario in which antenna correlation will be beneficial for both the UL and DL communications in FD C-RAN with the implementation of EFSC scheme. The effectiveness of the proposed methods and theorems are verified via simulation results.

On the Two-User MISO Interference Channel With Single-User Decoding: Impact of Imperfect CSIT and Channel Dimension Reduction

This paper examines the Rayleigh fading multiple-input single-output interference channel with single-user decoding and limited channel state information (CSI) feedback. Its contribution is twofold. First, the achievable rate-region with imperfect CSI at the transmitters due to channel quantization errors is analyzed for the two-user case. An analytically tractable inner bound on that region is derived, which provides insights into the problem. Similarly to the case of perfect CSI at the transmitters, it can be shown that beamforming constitutes an optimal strategy for achieving the boundary points of the inner bound. Second, we present a novel CSI feedback scheme that reduces the cross-link CSI feedback overhead, and enhances throughput for any rate-limited feedback scheme. This feedback reduction is crucial when control channels for sharing information between transmitters and unintended receivers are rate limited. A distinguishing feature of the proposed scheme is that feedback is taken into account already at the channel estimation stage. While maintaining a high array gain in the direct channel towards the intended user, the scheme induces an effective low-dimensional cross-link channel, which can be quantized more accurately than the full-dimensional channel. This eventually leads to enhanced performance while maintaining a low CSI feedback overhead. Finally, the proposed feedback scheme is extended to the case of $N>2$ users and shown to provide a significant reduction in CSI feedback overhead in a practically oriented setting.