Joint MCS Adaptation and RB Allocation in Cellular Networks Based on Deep Reinforcement Learning With Stable Matching

Device-to-Device (D2D) communication is envisioned as an energy-efficient technology in the (5G) cellular standard. This paper addresses the channel and power allocation for heterogeneous cellular network-supported D2D during downlink transmission. We propose an energy-efficient scheme in terms of a joint resource block (RB) and power allocation. The energy efficiency of D2D (EE-D2D) is maximized without jeopardizing the quality of service (QoS) requirements of the other tier users. The optimization scheme decomposes into two subproblems. First, the Sequential Max Search (SMS) resource block allocation algorithm is applied to D2D users. Second, a genetic optimization approach (GA) is used to optimize the power of the D2D transmitter and base stations. Through simulation, we evaluate the proposed scheme (SMS-GA) under different QoS requirements. The SMS-GA scheme achieved a higher energy efficiency of D2D as compared to the max power scheme (SMS-Max).

In this paper, we propose a dynamic spectrum allocation (DSA) scheme DeepBlocks at the backdrop of sixth-generation (6G) communication networks that address the challenges of fixed spectrum allocations (FSA). The scheme exploits the advantages of deep-Q-network (DQN) and minimizes the search state explosion through a reward-penalty framework. A dynamic allocation of unallocated resource blocks (RBs) to mobile units (MUs) is carried out and once the allocation of RBs is complete, we integrate blockchain (BC) to record the transactional ledgers. The resource usage of MUs is recorded through smart contracts (SCs). We model the proposed scheme as a convex optimization problem, and subproblems are decomposed into a Pareto-optimal solution via Techebyecheff decomposition. In the simulation, we compare our scheme against FSA, and fifth-generation (5G) based DSA schemes like reinforcement learning (RL), deep neural networks (DNN)-based, and duelling DQN based schemes. The comparative analysis of 6G-DQN is modeled in terms of reward formulation, scalability of 6G-DQN-assisted DSA, and profit scenarios of BC-based allocation through intelligent channel control. The scheme proposes significant findings, with the best fit learning rate of 0.0001, and takes 500 episodes to converge to 60 total resource blocks. The servicing latency of the scheme is 272.4 ms, compared to 2010 ms in the duelling DQN approach. In spectrum allocation, an improvement of 26.32% is observed against non-DQN approaches, and 13.57% in the fairness parameter for spectrum allocation due to BC inclusion. The findings present the scheme efficacy for DSA over the aforementioned conventional approaches.

Wireless mobile radio systems have to serve users with different quality of service (QoS) requirements. Scheduling algorithms like weighted proportional fair (WPF) have been proposed considering channel gains to improve the system capacity in terms of sector throughput as well as user priorities to fulfill QoS requirements. These scheduling algorithms allow assigning different priority factors to users and services so that different QoS requirements can be fulfilled. This paper provides an analysis of the influence of users with different priorities on the sector and user throughput. It is shown analytically and by system level simulations that the average user throughput can be adjusted by choosing a specific priority factor. Furthermore, it is shown that the sector throughput decreases if WPF scheduling for users with different priorities and full buffer model are considered compared to a scenario with users having equal priority. If QoS requirements for realistic traffic models, e.g. FTP, have to be fulfilled, for each high priority user low priority users have to be removed from the system in order not to exceed an acceptable number of unsatisfied users. It is shown that the decrease in sector throughput with a realistic traffic model and QoS requirements is higher than for the full buffer model.

This paper investigates the inter-cell interference coordination (ICIC) for heterogeneous networks (HetNets) with full-duplex (FD) small cell base stations to maximize the overall system throughput. To be compatible with the enhanced ICIC (eICIC) and the further enhanced ICIC (FeICIC) of existing LTE standards, the proposed FdICIC technique adopts cell range expansion and almost blank subframe to deal with the complicated interference scenario in such FD HetNets. In detail, the FdICIC includes four steps, namely, choosing the serving base station, pairing users for FD communication, resource block (RB) allocation, and power control of FD users. Through numerical simulation, we demonstrate that: (1) the overall system throughput can be improved by 10–25% with four FD small cell base stations when the residual self-interference is between − 130 to − 110 dB, (2) by carefully selecting the CRE bias, the overall system throughput can be further improved by about 30%, (3) the proposed user pairing, RB allocation, and power control steps have significant contributions to the performance improvement.

In this paper, a Long-Term Evolution-Advanced (LTE-A) Modified Best Channel Quality Indicator (M-BCQI) Scheduler is proposed for enhanced cell edge throughput performance. The scheduling algorithm utilizes two time slots, each of 0.5 ms in length, with the First Time Slot (FTS) implemented with the Best Channel Quality Indicator (BCQI) algorithm has a CQI-focused Resource Block (RB) allocation mechanism, while the Robin (RR) algorithm is the mechanism used in the Second Time Slot (STS) for RB allocation. Vienna LTE system level simulator was used for the proposed scheduling algorithm assessment. The results obtained showed significant improvement, indicating 44% superior cell edge performance and 56 % better fairness index in comparison to BCQI. However, M-BCQI slightly underperformed by 4.2% in terms of average throughput in comparison to BCQI and by 7% in comparison to RR in terms of fairness index.

Supporting ultra-reliable and low-latency communications (URLLC) is crucial for vehicular traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service-aware resource allocation framework for vehicle-to-vehicle (V2V) communication is proposed. The proposed scheme incorporates the physical proximity and traffic demands of vehicles to minimize the total transmission power over the allocated resource blocks (RBs) under reliability and queuing latency constraints. A Lyapunov framework is used to decompose the power minimization problem into two interrelated sub-problems: RB allocation and power optimization. To minimize the overhead introduced by frequent information exchange between the vehicles and the roadside unit (RSU), the resource allocation problem is solved in a semi-distributed fashion. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles into disjoint zones based on mutual interference. Second, a per-zone matching game is proposed to allocate RBs to each vehicle user equipment (VUE) based on vehicles’ traffic demands and their latency and reliability requirements. In the formulated one-to-many matching game, VUE pairs and RBs rank one another using preference relations that capture both the queue dynamics and interference. To solve this game, a semi-decentralized algorithm is proposed using which the VUEs and RBs can reach a stable matching. Finally, a latency-and reliability-aware power allocation solution is proposed for each VUE pair over the assigned subset of RBs. Simulation results for a Manhattan model show that the proposed scheme outperforms the state-of-art baseline and reaches up to 45% reduction in the queuing latency and 94% improvement in reliability.

With the advent of 5G era, network slicing has received a great deal of attention as a means to support a variety of wireless services in a flexible manner. Network slicing is a technique to divide a single physical resource network into multiple slices supporting independent services. In beyond 5G (B5G) systems, the main goal of network slicing is to assign the physical resource blocks (RBs) such that the quality of service (QoS) requirements of eMBB, URLLC, and mMTC services are satisfied. Since the goal of each service category is dearly distinct and the computational burden caused by the increased number of time slots is huge, it is in general very difficult to assign RB properly. In this paper, we propose a deep reinforcement learning (DRL)-based network slicing technique to find out the resource allocation policy maximizing the long-term throughput while satisfying the QoS requirements in the B5G systems. Key ingredient of the proposed technique is to reduce the action space by eliminating undesirable actions that cannot satisfy the QoS requirements. Numerical results demonstrate that the proposed technique is effective in maximizing the long-term throughput and handling the coexistence of use cases in the B5G environments.

In this paper, we propose an energy-efficient 128∼2048/1536-point partial FFT processor for the resource block mapping in the 3GPP-LTE OFDMA system. First, we present a modified partial cached-FFT algorithm with radix-2/22/3 operations for different resource block allocation in the OFDMA system. Then, based on the proposed algorithm, we build up the architecture of radix-2/22/3 cached-FFT processor. This processor can partially compute the selected resource blocks assigned by the base-station with the virtual resource block (VRB) to physical resource block (PRB) mapping in the 3GPP-LTE system. The proposed FFT processor was designed and implemented using TSMC 0.18µm 1P6M CMOS technology. Measurement results of the test chip show that the FFT processor runs at 35MHz with 11.29mW power consumption and it consumes 0.5 to 1.29nJ per FFT point.

In cellular standards such as 5G NR, the sub carriers of the OFDM waveform are dynamically scheduled in groups of 12, called resource blocks (RBs). This dynamic allocation of RBs causes difficulties in performing closed-loop digital pre-distortion (DPD) estimation. If a closed-loop DPD estimation is performed on a reduced allocation of RBs, it can produce DPD models that are incompatible with other RB allocations. A closed-loop DPD solution is proposed to stabilize the correction, even when reduced RBs are used in the estimation. Measurements on a 5G local-area base-station RF front-end are used to demonstrate the performance.

Device to Device D2D communication is recognized as a key technology of the evolving 5G networks. Its main objective is to allow direct communication between proximity devices without routing data via eNodeB. It can take place either overlay or underlay cellular network. In the first case, dedicated Resource Blocks (RBs) are always allocated for D2D communication, leading to an expected waste of spectral resources. While in the second, D2D pairs share common RBs with the residual cellular users, resulting to highly better network performances, such as spectral efficiency, energy efficiency, and coverage extension. But it implies new challenges especially for interference management. For this reason, we will focus in this paper on the design and implementation of an interference aware RB allocation algorithm for D2D communications in order to maximize advantages from its integration in 5G networks without disturbing basic cellular or other D2D communicating pairs. Unlike most of previous game theoretic studies on resource allocation problems considering only pure strategies, firstly we propose a mathematical game theory model for our resource allocation problem as an interpretation of mixed strategy game in normal form and investigate mixed strategy Nash equilibrium (MSNE) points. This concept allows users to adopt more accurate strategies in order to maximize their utilities according to random network specificities. Furthermore we design a distributed spectral efficient resource allocation algorithm, based on interference minimization schema, leading to performance maximization for the entire networks' communication. Finally, we interpret our MSNE points and final RBs allocation table and compare interference factor and spectral efficiency obtained from our algorithm to other previous solutions through computer simulations.

The paper considers discrete multitone (DMT) modulation for multiuser communications when multiple users are supported by the same system, a zero-padding redundancy is employed at the transmitter output, and linear redundancy removal is used at the channel output. These users may have differing quality of service (QoS) requirements, as quantified by bit rate and symbol-error rate specifications, and are each assigned an equal number of subchannels. Our goal is to minimize the transmitted power, given the QoS specifications and subject to the knowledge of the channel and the second-order statistics of the colored interference at the receiver input. In particular, we find an optimum bit-loading scheme that distributes the bit rate transmitted across the various subchannels belonging to each user, and, subject to this bit allocation, we determine the precise subchannels assigned to each user, the optimum transceiver characterized by the input/output block transforms, and the redundancy removal operation. A major conclusion is that even though the optimum bit-rate allocation differs from the single-user case, the optimum transceiver does not. Further, it is determined entirely by the channel and interference conditions, and is unaffected by the QoS requirements.

We propose a wireless multimedia DS-CDMA system with dynamic power assignment (DPA) for adaptive traffic control. The system supports service groups with different quality of service (QoS) requirements specified in terms of both transmission bit rate and bit error rate (BER). The optimal received power level for each service group is obtained by minimizing a cost function consisting of the weighted sum of the blocking rates of different service groups subject to their QoS requirements. The performance is compared to a similar system with fixed power assignment (FPA) in which the received power level for each service group is assigned based only on its transmission bit rate. Simulation results show that DPA algorithm can support much higher offered traffic loads, on merit of the blocking probability. By implementing the cost function with different weighting coefficients, we can assign different blocking probabilities to different service groups to cope with the demands in the system. The DPA algorithm therefore plays the role of a movable blocking boundary in the system which can accommodate multimedia traffic and priority-based capacity access.

This paper investigates a multi-unmanned aerial vehicle (UAV) enabled wireless communication, where a number of ground nodes (GNs) are scheduled to communicate with UAVs in the presence of jammers with imperfect location information. Considering different quality of service (QoS) requirements for a wide range of applications, we aim to improve the minimum throughput, the average throughput, and the delay-constrained minimum throughput of all GNs, respectively, via the joint design of UAVs' trajectories, GNs' scheduling and power allocation. However, the formulated optimization problems are difficult to solve due to the non-convex and combinatorial nature. To overcome this difficulty, we propose two block coordinate descent (BCD) based algorithms to solve them sub-optimally with the aid of slack variables, successive convex approximation (SCA) technique and S-procedure. Numerical results show that our proposed algorithms outperforms the benchmark algorithms and offers a considerable gain in the view of different QoS requirements, giving a certain practical significance.

We consider a Web server that can provide differentiated services to clients with different quality of service (QoS) requirements. The Web server can provide N/spl ges/1 classes of proportional-delay differentiated services (PDDS) to heterogeneous clients. An operator can specify fixed performance spacings between classes, namely, r/sub i,i+1/>1, for i=1,..., N-1. Requests in class i+1 are guaranteed to have an average waiting time which is 1/r/sub i,i+1/ of the average waiting time of class i requests. With PDDS, we can provide consistent performance spacings over a wide range of system loading and this simplifies many pricing issues. In addition, each client can specify a maximum average waiting time requirement to be guaranteed by the PDDS-enabled Web server. We show that, in general, the problem of assigning clients to service classes in order to optimize system efficacy is NP-complete. We propose two efficient admission control algorithms so that a Web server can provide the QoS guarantees and, at the same time, classify each client to its "lowest" admissible class, resulting in lowest usage cost for the admitted client. We also consider how to perform end-point dynamic adaptation such that admitted clients can submit requests at a lower class and further reduce their usage costs without violating their QoS requirements. We propose two dynamic adaptation algorithms: one is server-based and the other is client-based. The client-based adaptation is distributed and is based on a noncooperative game technique. We carry out experiments to illustrate the effectiveness of these algorithms under different utility functions and traffic arrival patterns (e.g., Poisson, MMPP, and Pareto). We report extensive experimental results to illustrate the effectiveness of our proposed algorithms.

Effective support of real‐time multimedia applications in wireless access networks, viz. cellular networks and wireless LANs, requires a dynamic bandwidth adaptation framework where the bandwidth of an ongoing call is continuously monitored and adjusted. Since bandwidth is a scarce resource in wireless networking, it needs to be carefully allocated amidst competing connections with different Quality of Service (QoS) requirements. In this paper, we propose a new framework called QoS‐adaptive multimedia wireless access (QoS‐AMWA) for supporting heterogeneous traffic with different QoS requirements in wireless cellular networks. The QoS‐AMWA framework combines the following components: (i) a threshold‐based bandwidth allocation policy that gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls by assigning a threshold to each class, (ii) an efficient threshold‐type connection admission control algorithm, and (iii) a bandwidth adaptation algorithm that dynamically adjusts the bandwidth of an ongoing multimedia call to minimize the number of calls receiving lower bandwidth than the requested. The framework can be modeled as a multi‐dimensional Markov chain, and therefore, a product‐form solution is provided. The QoS metrics—new call blocking probability (NCBP), handoff call dropping probability (HCDB), and degradation probability (DP)—are derived. The analytical results are supported by simulation and show that this work improves the service quality by minimizing the handoff call dropping probability and maintaining the bandwidth utilization efficiently. Copyright © 2006 John Wiley & Sons, Ltd.