Physical Resource Block Research Articles

Pool resource management based on early collision detection in random access of massive MTC over LTE

Collision in Random Access (RA) procedure of Long-Term Evolution (LTE) networks is the main problem in supporting massive Machine-Type Communications (MTC). Current solutions for resolving MTC Devices (MTCDs) collisions in the LTE incur additional overhead to existing MTCDs which may not be efficient for cost-effective MTCDs. Also, the lack of proper Physical Resource Blocks (PRBs) management scheme may waste a large number of PRBs and leads to poor RA performance upon massive access of MTCDs. To this end, a simple PRBs management scheme based on the early preamble collision detection is proposed in this paper to improve the RA throughput of massive MTC. By exploiting the pool of RA resources and upon collision detection, a proper number of PRBs is granted to each collided preamble by estimating the number of collided MTCDs. To sustain the number of consumed PRBs around a certain threshold, the number of contending MTCDs is regulated using an appropriate Access Class Barring (ACB) factor. Simulation results show that the RA throughput and average RA delay can be improved using the proposed solution compared to the previous schemes; while the expected number of consumed PRBs in each RA procedure is sustained below a certain threshold.

VoLTE cell capacity estimation using AMR‐WB codecs and considering VAF, SID, packet bundling, and TTI bundling

SummaryThe Voice over Long‐Term Evolution (VoLTE) cell capacity notably has an influence on Call Admission Control (CAC) in Long‐Term Evolution (LTE) network. In literature, many techniques have been studied to estimate the LTE cell capacity, but most of them are throughput‐based physical layer (PHY layer) estimate. These estimates do not consider delay requirement, transmission loss, and protocol overhead. In this paper, we have proposed novel analytical models for estimating the VoLTE cell capacity in terms of number of users by considering available physical resource blocks (PRBs) in each bandwidth, voice traffic‐based characteristics such as voice activity factor (VAF), silent insertion descriptor (SID), and radio‐specific techniques such as packet bundling and transmission time interval (TTI) bundling. Our developed analytical models are then used to investigate the effect of modulation and coding scheme (MCS) and Adaptive Multi‐RateWideband (AMR‐WB) voice codec on VoLTE cell capacity. Results show that higher order modulation scheme with lower bit voice codec accommodates maximum number of VoLTE users. Further, the impact of VAF, SID, packet bundling, and TTI bundling on VoLTE cell capacity is investigated in different radio conditions. Finally, using our proposed model, the effect of hybrid automatic repeat request (HARQ) retransmission on VoLTE cell capacity is investigated.

Design of a cell selection mechanism to mitigate interference for cell-edge macro users in femto-macro heterogeneous network

The Femto-Macro heterogeneous network is a promising solution to improve the network capacity and coverage in mobile network. However interference may rise due to femtocell deployment nearby to macro user equipment (MUE) within macrocell network coverage. Femtocell offers main priority in resource allocation to its subscribed femto user equipment (FUE) rather than unsubscribed MUE. MUEs will suffer severe interference when they are placed near or within the femtocell area range especially at the cell edge. This phenomenon occurs due to the distance is far from its serving macro base station (MBS) to receive good signal strength. This paper presents a design of cell selection scheme for cell-edge MUE to select an optimal femto base station (FBS) as its primary serving cell in physical resource block allocation. In this study, the proposed cell selection consists of four main elements: measuring the closest FBS distance, Signal to Interference-plus- Noise-Ratio (SINR), physical resource block (PRB) availability and node density level for the selected base station. The main goal is to ensure celledge MUE has priority fairly with FUE in physical resource block allocation per user bandwidth demand to mitigate interference. Hence, the cell-edge MUE has good experienced on receiving an adequate user data rate to improve higher network throughput.

Radio resource allocation in multi-cell and multi-service mobile network based on QoS requirements

Modern networks offer many service types that need to be considered for a fair distribution of the available radio resources. One of the main strategies of the next generation of mobile communications to deal with that traffic demand is to reduce the distances between transmitters and receivers, in order to maximize the number of available channels per area. However, this solution also comes with a drawback: the inter-cell interference becomes more significant. In this work, we present new channel-aware Physical Resource Block (PRB) assignment problems for planned and unplanned small-cell networks. The problems are modeled as Integer Linear Programming (ILP) problems in order to handle the inter-cell interference issue, considering minimum data rate requirements per User Equipment (UE). We consider a Cloud-Radio Access Network (C-RAN), in which the PRB assignment within the network can be globally managed. Specifically for planned networks, the proposed formulation guarantees a minimum data rate in different coverage regions of each cell based on UEs’ traffic services. The proposed problems differ from other C-RAN PRB assignment approaches from the literature in the sense that our proposal guarantees solutions with minimum satisfaction for each mobile service provided by the network. Since the optimal solutions of the proposed problems have exponential complexity, this work also proposes a low-complexity heuristic solution which has polynomial complexity. In the most challenging simulated scenario, the proposed heuristic achieves 3.5% of data rate loss compared with the optimal solution, while a benchmark from the literature achieves 18.5%. In another scenario, we evaluate the outage probability, i.e., the relative frequency in which an algorithm is not capable of finding a feasible solution to the problem. In that scenario we show that the outage rate of the optimal and proposed solutions are similar, achieving 15% and 17.5%, respectively, whereas the benchmark solution presents a significantly higher outage rate of 36%.

On the Feasibility of 5G Slice Resource Allocation With Spectral Efficiency: A Probabilistic Characterization

An important concern that 5G networks face is supporting a wide range of services and use cases with heterogeneous requirements. Radio access network (RAN) slices, understood as isolated virtual networks that share a common infrastructure, are a possible answer to this very demanding scenario and enable virtual operators to provide differentiated services over independent logical entities. This article addresses the feasibility of forming 5G slices, answering the question of whether the available capacity (resources) is sufficient to satisfy slice requirements. As spectral efficiency is one of the key metrics in 5G networks, we introduce the minislot-based slicing allocation (MISA) model, a novel 5G slice resource allocation approach that combines the utilization of both complete slots (or physical resource blocks) and mini-slots with the adequate physical layer design and service requirement constraints. We advocate for a probabilistic characterization that allows to estimate feasibility and characterize the behavior of the constraints, while an exhaustive search is very computationally demanding and the methods to check feasibility provide no information on the constraints. In such a characterization, the concept of phase transition allows for the identification of a clear frontier between the feasible and infeasible regions. Our method relies on an adaptation of the Wang-Landau algorithm to determine the existence of, at least, one solution to the problem. The conducted simulations show a significant improvement in spectral efficiency and feasibility of the MISA approach compared to the slot-based formulation, the identification of the phase transition, and valuable results to characterize the satisfiability of the constraints.

Patient-Centric Cellular Networks Optimization Using Big Data Analytics

Big data analytics is one of the state-of-the-art tools to optimize networks and transform them from merely being a blind tube that conveys data, into a cognitive, conscious, and self-optimizing entity that can intelligently adapt according to the needs of its users. This, in fact, can be regarded as one of the highest forthcoming priorities of future networks. In this paper, we propose a system for Out-Patient (OP) centric Long Term Evolution-Advanced (LTE-A) network optimization. Big data harvested from the OPs' medical records, along with current readings from their body-connected medical IoT sensors are processed and analyzed to predict the likelihood of a life-threatening medical condition, for instance, an imminent stroke. This prediction is used to ensure that the OP is assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their critical data to their healthcare provider with minimal delay. To the best of our knowledge, this is the first time big data analytics are utilized to optimize a cellular network in an OP-conscious manner. The PRBs assignment is optimized using Mixed Integer Linear Programming (MILP) and a real-time heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization (WSRMax) approach and the Proportional Fairness (PF) approach. The approaches increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, however, the PF approach reported higher SINRs for the OPs, better fairness and a lower margin of error.

On the Application of Massive MIMO Systems to Machine Type Communications

This paper evaluates the feasibility of applying massive multiple-input multiple-output (MIMO) to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel, devised to exclusively consume data traffic from machine type communications (MTC) devices, the capacity (i.e., number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time–frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without the previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically, as well as the MMSE estimator, with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of the sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines toward a better understanding of the use of massive MIMO in MTC networks.

Collision-Aware Resource Access Scheme for LTE-Based Machine-to-Machine Communications

Long-term evolution (LTE) network is considered as the most promising infrastructure to provide efficient connectivity for a large number of autonomous machine-type communications devices (MTCDs). In order to improve the random access (RA) throughput of massive access machine-to-machine (M2M) communications over LTE networks, we propose a collision-aware resource access (CARA) scheme to reduce the collisions on the granted physical resource blocks (PRBs). In CARA, the evolved Node B exploits early collision detection of preambles at the first message and informs the MTCDs in the corresponding random access response (RAR) message if it can detect the collision successfully. The collided MTCDs then contend for PRBs access using an appropriate probability for efficient use of granted PRBs. Analytical and numerical performance evaluations of the CARA show that the RA throughput can be improved in comparison with the traditional RA procedure.

Techniques for Interference Mitigation Using Cooperative Resource Partitioning in Multitier LTE HetNets

Almost blank subframes (ABSs) have been proposed in the literature as a means to provide interference-free resources to mobile users. The use of ABS becomes inevitable in LTE heterogeneous cellular networks because of severe interference to small cells. Picocell edge users are typically the victims because of high interference exposure from macrocell tier. In case of hotspots with high-user density, picocells cannot provide coverage to the entire hotspot, thus forming a dense ring of macrocell users around picocells. These macrocell users are highly affected by picocell tier and hence should also be considered as victim users (VUEs). We propose that to protect these macrocell VUEs, macrocells and picocells should operate in a cooperative manner, such that not only macrocells should use ABS, but picocells should also use ABS to provide interference-free resources. We also introduce the concept of universal blanking pattern to coordinate among various base stations. We propose two methods for joint ABS density calculation for macrocells and picocells—bitrate utility-based optimization and physical resource block (PRB) allocation ratio-based formulation. Users with different bitrate demands are considered generating more realistic simulation scenarios. Exhaustive simulations illustrate that the proposed schemes offer significant improvement in VUE throughput as well as overall system throughput.

QoS‐aware downlink radio resource management in OFDMA‐based small cells networks

Interference management and supporting quality-of-service (QoS) requirements are the main challenges for the spectral resource allocation in orthogonal frequency division multiple access (OFDMA) small cells networks. In this study, the authors propose an algorithm for assigning physical resource block (PRB) with QoS constraints to eliminate interference among femtocells. They formulate the proposed resource allocation scheme as an optimisation problem where interference among femtocells is entirely avoided and multiple QoS for different services are supported. The proposed optimisation problem imposes the fairness among different femtocells and maximises the PRB efficiency. However, this problem is NP-complete, and thus, they offer a greedy algorithm for solving the problem. Simulation results show the throughput of their greedy algorithm is close to the optimal solution of the optimisation problem. Moreover, the proposed algorithm improves the network throughput by over 30–50% in different simulation scenarios with various femtocells densities in comparison to the previous methods. Furthermore, simulations illustrate the rejection ratio for all classes of services is lower than 3 % . Besides, the delays of different services are lower than the delay constraints.

Throughput and Power Efficiency Analysis Considering Traffic Loads in FFR with Movable Boundary

ABSTRACTIn Fractional frequency reuse (FFR) technique, the available physical resource blocks are reused among different cells in a manner that co-channel interference and/or inter-cell interference effect is reduced. However, when the resources are highly utilized, the sharing of these resources becomes exhausted and/or limited, and this leads to a high interference and hence high call blocking probability. Therefore, this paper proposes a call admission control in FFR to improve the performance of the FFR-based cellular system. The results of the two quality of service (QoS) constraints, the call blocking probabilities and the resource utilizations for users are used for some design criteria of the cell inner/outer area to maximize the throughput and hence for the evaluation of the system power efficiency.

FPGA Implementation of UFMC Based Baseband Transmitter: Case Study for LTE 10MHz Channelization

Universal filtered multicarrier (UFMC) is a low complexity promising waveform that provides quasi‐orthogonal property among subcarriers. In addition, it can achieve much better out‐of‐band emission performance than orthogonal frequency division multiplexing (OFDM) system. Authors have proposed a hardware platform to implement a UFMC transmitter in this paper. Highly reduced complexity schemes for IFFT, filtering, and spectrum shifting are realized on actual hardware. This helps to achieve overall architecture of the transmitter at the cost of minimal FPGA resource usage. Hence, the overall design uses only 1038 slice registers, 1154 slice LUTs, and 64 multipliers of Xilinx Virtex‐7 XC7VX330t device. A throughput of 773.5 Msamples/sec at an operational frequency of 364 MHz is achieved. This throughput is adequate for processing 50 Physical Resource Blocks (PRB) of LTE 10 MHz channelization in required time. The presented architecture provides a latency of only 2% of one LTE 10MHz channelization symbol due to the implementation of pipelining at different levels. Although the presented hardware design in its current form meets LTE 10MHz channelization throughput requirements, further increase in throughput is possible due to the scalable nature of the architecture. To the best of our knowledge, this work is first ever FPGA solution for UFMC transmitter presented in the literature.

Non-Orthogonal Pilot Designs for Joint Channel Estimation and Collision Detection in Grant-Free Access Systems

In this paper, we consider a densely deployed phantom cell system providing a low latency network for massive connectivity. Non-orthogonal pilot designs serve as a promising solution to support a large number of users, but fast collision detection at the receiver is needed for low latency networks. Recently, a nice solution based on an on–off type non-orthogonal pilot design with collision detection capability has been proposed. It can serve more users than the orthogonal pilot design but at the cost of degraded channel estimation performance compared to the orthogonal optimum pilot design. We propose a new non-orthogonal pilot design with collision detection capability and improved channel estimation performance. An optimum threshold-based detection criterion is developed. We further show that a dynamically calculated optimum threshold-based detection outperforms a fixed threshold-based detection. Next, we investigate non-orthogonal pilot designs for fractional bandwidth allocation. We propose two new non-orthogonal pilot designs for physical resource block based resource allocation. Both of the new designs support fast collision detection at the receiver. Performance evaluation results show that the proposed schemes provide equivalent or better channel estimation performance and support much more users than the orthogonal pilots defined in the current 4G standards. Finally, we explore sparse channel estimation with compressed sensing technique. We prove several propositions regarding compressed sensing based estimation performance. Using these propositions, two novel orthogonal pilot designs are developed for sparse channel estimation with optimized performances. Finally, utilizing these orthogonal pilot sets, we propose a non-orthogonal pilot design with collision detection capability for sparse channel estimation.

A flexible resource scheduling scheme for an adaptive SCMA system

Sparse code multiple access (SCMA) is a new type of non-orthogonal multiple access technology, and it originates from low-density signature (LDS) technology. SCMA can obtain an additional coding gain through the codebook design. Combining the concepts of OFDMA and CDMA, SCMA has the advantages of high capacity, low time delay and high data rate. We propose adaptive allocation technology (AAT) and design an adaptive SCMA system. The designed system has better flexibility and adaptability than conventional OFDMA and SCMA systems. However, preventing fragmentation of physical resource blocks (PRBs) in the resource scheduling is crucial for rendering the adaptive SCMA system feasible. For this purpose, we design two optional resource scheduling schemes: resource first and user first. The theoretical derivation and simulation results demonstrate the feasibility and superiority of the proposed algorithms and schemes in terms of system throughput and packet loss rate (PLR) performance.

Random Graph Coloring-Based Resource Allocation for Achieving User Level Fairness in Femtocellular LTE-A Networks

Increasing demand of high data rates and spectral efficiency is sailing next generation wireless networks toward smaller cells structure known as femtocells with air interference technology known as orthogonal frequency division multiple access. To mitigate the high cost of cellular long term evolution-advanced (LTE-A) operators and poor indoor coverage, femtocell network structure has been proposed for underlaying evolved Node-B. Resource allocation, interference management and user level fairness are among the main problems for underlaying femtocellular network deployment. Radio resource management problem becomes even more complicated, when randomly deployed femtocells serve large number of users within total available physical resource blocks (PRBs) of LTE-A networks. To solve the radio resource management problem efficiently for accommodating maximum number of users within total available PRBs, network interference in femtocellular network is modeled as Bernoulli random graph model. A bound over required number of PRBs in the network has been established based on number of users connected with each femtocell and their interference relation with other femtocells. In this paper we have proposed the priorities greedy graph coloring based resource allocation algorithm for femtocellular LTE-A networks. The proposed algorithm accommodate maximum number of users, satisfies the total available PRBs, interference and user level fairness in the femtocellular networks. Our simulation analysis shows the performance comparison between proposed and existing algorithms. The proposed algorithm perform better than existing algorithms in term of maximum number of users that can be served by femtocells fairly, using minimum number of PRBs. User level fairness index obtained in our proposed algorithm is more efficient compared to reported works. Our proposed algorithm improves the balance among interference, resource utilization and user-level fairness in the system within an efficient convergence time complexity of $$O(m^2n)$$ even in dense deployment of femtocellular networks with varying number of users, where, m is the number of users and n is number of femtocells in the networks.

Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

Efficient Fast-Convolution-Based Waveform Processing for 5G Physical Layer

This paper investigates the application of fast-convolution (FC) filtering schemes for flexible and effective waveform generation and processing in the fifth generation (5G) systems. FC-based filtering is presented as a generic multimode waveform processing engine while, following the progress of 5G new radio standardization in the Third-Generation Partnership Project, the main focus is on efficient generation and processing of subband-filtered cyclic prefix orthogonal frequency-division multiplexing (CP-OFDM) signals. First, a matrix model for analyzing FC filter processing responses is presented and used for designing optimized multiplexing of filtered groups of CP-OFDM physical resource blocks (PRBs) in a spectrally well-localized manner, i.e., with narrow guardbands. Subband filtering is able to suppress interference leakage between adjacent subbands, thus supporting independent waveform parametrization and different numerologies for different groups of PRBs, as well as asynchronous multiuser operation in uplink. These are central ingredients in the 5G waveform developments, particularly at sub-6-GHz bands. The FC filter optimization criterion is passband error vector magnitude minimization subject to a given subband band-limitation constraint. Optimized designs with different guardband widths, PRB group sizes, and essential design parameters are compared in terms of interference levels and implementation complexity. Finally, extensive coded 5G radio link simulation results are presented to compare the proposed approach with other subband-filtered CP-OFDM schemes and time-domain windowing methods, considering cases with different numerologies or asynchronous transmissions in adjacent subbands. Also the feasibility of using independent transmitter and receiver processing for CP-OFDM spectrum control is demonstrated.

Design and stochastic geometric analysis of an efficient Q-Learning based physical resource block allocation scheme to maximize the spectral efficiency of Device-to-Device overlaid cellular networks

Device-to-Device (D2D) communication empowers direct communication between two proximal users and helps in offloading network traffic. It is considered as one of the most promising 3GPP Long Term Evolution (LTE)-Advanced technologies for improving the spectrum and energy efficiency of Proximity-based Services (ProSe). However, due to interference posed by the D2D transmitters to the primary cellular users, intelligent resource allocation techniques need to be incorporated for the betterment of the overall system. Overlay D2D communication avoids the interference between cellular and D2D users by dedicating resources for D2D users. With this approach, efficient use of the dedicated Physical Resource Blocks (PRBs) among D2D users is still a concern. To handle the issue of optimum spectral efficiency, efficient allocation of spectrum resources is essential among D2D users. For the first time, we address this problem and propose a PRB allocation scheme that maximizes the PRB utilization among D2D users. The proposed scheme spreads the PRB requirement of a D2D pair over multiple PRBs while reducing the transmit power over each PRB, thereby reducing the overall interference and improving the spectral efficiency of the network. Extensive simulations demonstrate that our scheme does better than the conventional PRB allocation scheme (of assigning PRBs based on their data rate demand), by increasing the overall throughput and energy efficiency of the network. To analytically evaluate the proposed PRB allocation scheme, we present a stochastic geometry based framework to analyze the coverage and average rate of the network, and conclude that the link SINR of the D2D pairs is actually improved on employing our proposed scheme which results in high coverage and overall rate of the system. Finally, we propose a Q-Learning algorithm that learns the optimum number of PRBs to dedicate for overlay D2D communication based on the PRB demand of the cellular and D2D users. Extensive simulations reveal that the proposed algorithm efficiently allocates PRBs to the D2D pairs when compared to the conventional static scheme of dedicating fixed number of PRBs.

A Mobile Network Planning Tool Based on Data Analytics

Planning future mobile networks entails multiple challenges due to the high complexity of the network to be managed. Beyond 4G and 5G networks are expected to be characterized by a high densification of nodes and heterogeneity of layers, applications, and Radio Access Technologies (RAT). In this context, a network planning tool capable of dealing with this complexity is highly convenient. The objective is to exploit the information produced by and already available in the network to properly deploy, configure, and optimise network nodes. This work presents such a smart network planning tool that exploits Machine Learning (ML) techniques. The proposed approach is able to predict the Quality of Service (QoS) experienced by the users based on the measurement history of the network. We select Physical Resource Block (PRB) per Megabit (Mb) as our main QoS indicator to optimise, since minimizing this metric allows offering the same service to users by consuming less resources, so, being more cost-effective. Two cases of study are considered in order to evaluate the performance of the proposed scheme, one to smartly plan the small cell deployment in a dense indoor scenario and a second one to timely face a detected fault in a macrocell network.

The Impact of Efficient Transport Blocks Management on the Downlink Power in MIMO Spatial Multiplexing of LTE-A

Radio spectrum for mobile operators is an expensive resource that should be wisely provisioned to meet users’ demands. In long term evolution-advanced, the number of physical resource blocks (PRBs) are used to gauge the users’ needs. Accordingly, there is a time in the network when it is beneficial to gain the advantages of consuming the unused PRBs in the transport block (TB) context to operate at a significantly lower power cost per PRB. The novelty of the letter is that the $3\text{rd}$ generation partnership project (3GPP) standard tables are used with multiple-input-multiple-output (MIMO) spatial multiplexing mode, to control the transport block size (TBS) index. This can be done by setting more PRBs and hence changing the modulation order that leads to reducing the transmitted power. In addition to that, MIMO layers, the prerequisite throughput, the intercell interference as well as the channel conditions of each user are considered as constraints to improve the downlink power efficiency. To describe the abovestated problem, an optimization problem is formulated as binary integer nonlinear program, which is very difficult to solve. Thus, a reformulation linearization technique based on a branch-and-bound framework is used to simplify the aforementioned BINLP.