Integrated sensing and communication (ISAC) can enhance artificial intelligence-generated content (AIGC) networks by providing efficient sensing and transmission. Existing AIGC services usually assume that the accuracy of the generated content can be ensured, given accurate input data (e.g., pose image) and command (i.e., prompt), thus only the content generation quality (CGQ) is concerned. However, it is not applicable in ISAC-based AIGC networks, where content generation is based on inaccurate sensed data. Moreover, the AIGC model itself introduces generation errors, which depend on the number of generating steps (i.e., computing resources). Thus, to assess the quality of experience (QoE) of ISAC-based AIGC services, this paper proposes a content accuracy and quality aware service assessment metric (CAQA). Since allocating more resources to sensing and generating improves content accuracy but may reduce communication quality, and vice versa, this sensing-generating (computing)-communication three-dimensional resource tradeoff must be optimized to maximize the average CAQA (AvgCAQA) across all users with AIGC (CAQA-AIGC). This problem is NP-hard, with a large solution space that grows exponentially with the number of users. To solve the CAQA-AIGC problem with low complexity, a standard linear programming (LP) guided deep reinforcement learning (DRL) algorithm with an action filter (LPDRL-F) is proposed. Through the LP-guided approach and the action filter, LPDRL-F can transform the original three-dimensional solution space to two dimensions, reducing complexity while improving the learning performance of DRL. Simulations show that compared to existing DRL and generative diffusion model (GDM) algorithms without LP, LPDRL-F converges faster and finds better resource allocation solutions, thus improving AvgCAQA by more than 10%. With LPDRL-F, CAQA-AIGC can achieve an improvement in AvgCAQA of more than 50% compared to existing schemes focusing solely on CGQ.

Mobile Edge Computing (MEC) is promising to enable low delay services with which users can offload computing intensive and delay sensitive tasks to the edge. Considering a multi-cell MEC (MC-MEC) network without sufficient resources to serve all users, user selection and non-orthogonal multiple access (NOMA) should be introduced. Then, to maximize the delay-aware average user service satisfaction degree (DA-AveUSD), user selection and resource allocation are jointly optimized (DA-JUSRA), which is modeled as a mixed integer nonlinear programming (MINLP) problem and proven to be NP-hard. To solve this problem, it is decomposed into two independent subproblems, i.e., the power allocation (PA) problem and the user selection, subchannel scheduling and computing resource allocation (USC) problem. Next, a convex evolutionary alternating optimization (CEAO) algorithm is proposed, which alternately applies the convex optimization method and the Karush-Kuhn-Tucker (KKT)-embedding enhanced elite genetic algorithm KKT-embedding E2GA to solve the PA and the USC problem, respectively. Simulations show that compared to the optimal exhaustive search algorithm, the proposed CEAO algorithm converges rapidly within a few iterations, with a gap in DA-AveUSD of less than 1% to the optimum performance. Next, compared to existing user selection schemes, DA-JUSRA with CEAO can enhance DA-AveUSD by more than 50% and yield a higher optimal load.

Ultra-reliable and low-latency communication (uRLLC) is an important feature for the fifth-generation mobile network and beyond ($\mathbf{5 G} \boldsymbol{/} \mathbf{B 5 G}$). It has stringent requirements for ultra-high reliability (e.g., 1e-5) and extremely low user plane latency (e.g., 1ms). For cell edge user equipments (UEs) with severe inter-cell interference, uRLLC availability, i.e., defined as the probability of satisfying both reliability and latency requirements simultaneously, tends to be low. Under the constraint of 5G new radio (NR) protocols, in order to improve the availability of uRLLC, the bottleneck mainly lies in the guarantee of transmission reliability. Coordinated multi-point (CoMP) transmission is an enabling technique to improve the uRLLC availability via spatial diversity. This paper mainly focuses on the availability analysis of CoMP-enabled uRLLC with NR protocols. The uRLLC availability can be equivalent to a full probability model with the conditional probability of reliability given a latency constraint. Firstly, considering the NR frame structure and the procedure of the user plane processing with hybrid automatic repeat request (HARQ), a maximum transmission count is obtained given the latency requirement. Then, the system uRLLC availability is derived with the maximum transmission count for typical and edge UEs using stochastic geometry (SG). Finally, the analytical results are validated by Monte Carlo simulations. Moreover, the results show that in a single transmission, the system uRLLC availability with CoMP can be improved by 39.20 % for typical UEs and 322.44% for edge UEs compared with No-CoMP.

The maximum distance separable (MDS) coding based caching is effective to reduce the delivery delay of content in low earth orbit (LEO) satellite networks. However, due to the severe packet loss of satellite links and the variations among them, the existing distance aware delivery node selection methods may lead to significant packet loss and delivery delay. To solve this problem, a packet loss aware delivery node selection method is proposed in this paper. First, the delivery delay of coded sub-contents is analyzed by taking packet loss into account. And the delivery node selection problem is formulated as a delivery delay minimization problem. Then, the problem is divided into multiple sub-problems according to the time slot, where each sub-problem is a single-constraint knapsack problem. Based on the Edmonds theorem, the greedy algorithm is used to obtain the optimal solution. Finally, simulations are carried out on a walkerdelta constellation to verify the performance of the proposed method. And the results show that the user perceived packet loss and the packet retransmission induced delivery delay can be reduced by 83% and 42.8%, respectively, when compared with the existing distance aware delivery node selection method.

In mobile edge computing (MEC) enabled Industrial Internet of Things (MEC-IIoT), task offloading and resource allocation are always jointly optimized to achieve the best energy efficiency of IIoT terminals, which is important for MEC-IIoT. Existing research mainly focused on the instant energy consumption for the current task, ignoring the fact that the energy consumption is long term since energy is also required for subsequent tasks. Excessive energy consumption by the current task will affect the execution of subsequent tasks, leading to a degraded quality of service (QoS). Meanwhile, to solve the joint optimization problem, existing multi-agent deep reinforcement learning (MADRL) based methods face challenges like slow convergence. To tackle these problems, this paper proposes a long-term energy control enabled joint optimization scheme for the task offloading and resource allocation (LTE-JTORA). The main idea is to maximize the terminal energy efficiency while enabling the terminal to complete as many tasks as possible, so that the long-term energy control is realized. Then, an energy efficiency (EE) reward based collaborative MADRL (EE-CMADRL) is proposed to solve the NP-hard optimization problem. Different to MADRL where multiple agents work independently, EE-CMADRL is based on the Centralized Training Distributed Execution (CTDE) framework, where multiple agents can work collaboratively to train one EE reward enabled critic network. With more diverse data from multiple actor networks, EE-CMADRL can converge fast. Simulation results show that, compared to existing MADRL algorithms, the proposed EE-CMADRL improves the convergence speed by 71%. Compared to existing schemes with instant energy control, with the same time and energy constraints, the proposed LTE-JTORA scheme increases the number of completed tasks by 57%, and improves energy efficiency by 68%.

With the growing demand for high spectral efficiency in large-scale ad hoc networks, traditional half-duplex communication fails to meet the requirements. Although in-band full-duplex (IBFD) communication improves spectral efficiency, there is serious mutual interference (MI) in high-density node environments. This paper proposes a novel Frequency Hopping and Duplex Mode Selection (FH-DMS) method for MI suppression. By leveraging stochastic geometry theory, the throughput of the IBFD network with FH-DMS is derived. Furthermore, the optimization of the number of frequency hopping points and the FD mode selection probability is analyzed to maximize throughput. Simulation results show that the proposed method reduces MI intensity nearly tenfold and increases throughput by approximately ten times, particularly in high-density, self-interference-limited high-quality communication scenarios.

Task-oriented communications with semantic encoders are promising to enhance the communication efficiency, by selecting and transmitting valuable data according to task requirements/queries. However, existing semantic encoders lack the capability to track the changing in queries, leading to biased data selection. This paper proposes a query-aware semantic encoder, i.e., Query-Data Cross (QDC) encoder for task-oriented communications. By consistently focusing on data features that are most relevant to the current query at the transmitter, QDC can adapt to changing queries. Based on the dynamic semantic relevance obtained by QDC, a relevance-based data selection and bandwidth allocation optimization (RDSBA) problem is formulated, considering a multi-device task-oriented communication system, where devices should transmit valuable data with high relevance to the queries broadcasted by the base station (BS). RDSBA aims to maximize the data profit of all devices, which is defined as the difference between the relevance of data selected for the BS and the cost of obtaining the data. Then, a DRL-based data selection and bandwidth allocation (DRL-DB) algorithm is proposed to solve the NP-hard optimization problem. Simulation results demonstrate that QDC can smartly track the changing in queries and achieve an accuracy of at least 85% in relevance evaluation, more than 8% higher than existing schemes. Based on the relevance provided by QDC, the proposed RDSBA scheme with DRL-DB can increase the data profit by at least 18%, comparing to existing schemes.

Decentralized coded caching (DCC) is promising to relieve the load pressure of the networks (i.e., reducing the delivery rate) by creating multicast opportunities for a group of users. However, DCC suffers from high complexity (i.e., exponential of user number) because multicast opportunities are obtained by traversing all the possible user subsets. Considering individual user preferences, this article proposes a low-complexity user-pair-based modified DCC scheme (UP-MDCC). It only traverses user subsets with two users (i.e., user-pair) to generate coded subpackages, while obtaining similar delivery rate performance as DCC. To achieve this, we first update the traditional DCC to modified DCC (MDCC). Different to DCC which caches all N contents uniformly, MDCC can choose the favorite$N_{s}$contents to cache, such that more caching capacity can be allocated to the contents with higher requesting probability. Then, given MDCC, the relationship between the delivery rate and the number of cached contents is derived. It is revealed that when the favorite contents are entirely cached for each user, more than 99% of the delivery rate gain is generated by the user subsets containing only two users. Therefore, a low-complexity UP-MDCC can be proposed by limiting MDCC to traverse only the user subsets containing two users. Moreover, to find the optimal user-pairs to provide the minimum delivery rate for UP-MDCC, a graph-partitioning-based user-pairing strategy (GP-UPS) is proposed. Simulations verify that with GP-UPS, UP-MDCC can achieve similar delivery rate as that of uncoded placement absolutely fair (UPAF) caching and MDCC with a gap of less than 1%. Moreover, compared to DCC, the number of user subsets traversed is significantly reduced in UP-MDCC and the complexity is reduced from exponential to square of user number.

Collaborative mobile edge computing enables resource-constrained edge facilities to work cooperatively for computation-intensive tasks. However, as the number of tasks demanded by various applications increases, resource competition is inevitable in edge facilities. Existing works tackle the resource competition problem with a first come first served (FCFS) scheme, which is blind to different delay requirements among tasks. This may result in tasks with higher delay requirements waiting a long time for service, thereby reducing overall service quality. This paper proposes a prioritized queuing scheme with task dependency (PQTD), which allows high-prioritized sub-tasks with higher delay requirements to jump into the queue ahead of low-prioritized sub-tasks with lower delay requirements. To describe the complicated delay change caused by queue-jumping, a joint DAG-queue delay (JDQD) model is proposed, which analyzes the chain reaction of delay changes caused by the processing queue on the server and the task dependency. With JDQD, a multi-task assignment optimization problem is formulated to maximize the average satisfaction degree (AvgSatD), which is defined according to the priorities of the tasks and their delay requirements. Then, a tree-based algorithm is proposed to solve the NP-hard optimization problem, i.e., Monte Carlo Tree Search (MCTS). Simulation results demonstrate the effectiveness of the PQTD queuing scheme and tree search mechanism of MCTS. Overall, PQTD + MCTS can increase AvgSatD by at least 45.8% with an acceptable complexity.

Federated Learning (FL) empowers wireless intelligent applications, by leveraging distributed data of edge clients for training without compromising privacy. Client selection is inevitable in FL, since clients have diversified learning capabilities arising from heterogeneous computing and communication resources. Existing methods like fair-selection and dropping-straggler are either inefficient or unfair (resulting in a less effective trained model). Therefore, we propose FedAoI, an Age-of-Information (AoI) based client selection policy. FedAoI ensures fairness by allowing all clients, including stragglers, to submit their model updates while maintaining high training efficiency by keeping round completion times short. This trade-off is achieved by minimizing Peak-AoI (PAoI), the interval between a client's consecutive participations. An optimization problem is formulated by minimizing the Expected-Weighted-Sum-of-PAoI. This NP-hard problem is addressed with a two-step sub-optimal algorithm, PriorS. It first calculates client priority in a round using Lyapunov optimization and then selects the highest-priority clients through G-FPFC (Greedy minimization of the round weighted-sum-of-PAoI with First-Priority-First-Considered). Simulation results demonstrate that, compared to fair-selection, FedAoI improves average efficiency by 83.8% and achieves an average model accuracy of 97.3% (or at the cost of averaging 2.7% degradation in model accuracy). Compared to dropping-straggler, FedAoI reduces the average model accuracy degradation from 9.5% to 2.7%.

Due to the high mobility of vehicles, service migration is inevitable in vehicular edge computing (VEC) networks. Frequent service migrations incur prohibitive migration cost including the computing cost (e.g., increased computing delay) and communication cost (e.g., occupied backhaul bandwidth). Yet existing service migration schemes are usually designed without considering the impact of the computing cost. This paper considers the impact of computing and communication cost jointly, and proposes a computing and communication cost-aware service migration scheme for VEC networks (i.e., CA-migration). Taking the service delay as a QoS metric for VEC networks, this paper formulates a migration optimization problem aiming to maximize the services' satisfaction degree of delay (i.e., the probability that the service delay is smaller than the service delay requirement), where both the communication cost and computing cost affect the services' satisfaction degree. Since the optimization problem is a constrained non-linear integer programming problem, it is difficult to solve. Moreover, the VEC networks are highly dynamic. Thus, a fast transfer reinforcement learning (fast-TRL) method combining transfer learning and reinforcement learning is proposed to provide an adaptive service migration scheme in dynamic VEC networks. Simulation results show that compared with existing schemes, the proposed CA-migration scheme can increase the satisfaction degree by up to 30%, and needs 25% less training time to obtain the optimal service migration policy.

Federated learning (FL) over mobile networks has attracted intensive attention recently. User mobility is a fundamental feature of mobile networks, which leads to dynamic network topology and wireless connectivity losses. As such, user mobility is usually considered a “trouble maker” and a great challenge to FL over mobile networks. Interestingly, we found that small user mobility can positively contribute to improving FL performance. This is because the total dataset size and the data diversity that the FL can utilize are increased by user mobility. Based on this observation, we aim to tame and exploit mobility instead of treating it as a hostile “trouble maker”. To this end, we first investigate how the FL performance changes with user mobility theoretically by jointly taking into account the positive and negative aspects of mobility. Specifically, a closed-form expression to quantify the impact of mobility on the FL loss is derived, which explains when negative or positive aspects of mobility dominate the FL performance. Next, a joint FL and communication optimization problem is formulated based on theoretical analyses to minimize the FL loss function by optimizing wireless resource allocation. Finally, we propose a two-step optimization algorithm to solve the formulated problem. The simulation results verify the theoretical analyses. It is also shown that the proposed method can significantly enhance learning performance considering users with high mobility. When the average velocity is larger than 150 km/h, the proposed method achieves more than 80% accuracy in the MNIST dataset, while the existing methods may fail during training.

Sloped terrain is common in practice. This paper proposes a lateral controller for controlling the autonomous ground vehicle (AGV) to track the reference path on sloped terrain. The impact of sloped terrain on AGV’s motion is analyzed. The lateral disturbance from gravity is modeled, and a feedforward compensator is designed to counteract the effect of the lateral disturbance. A lateral controller consisting of a feedforward compensator and a feedback controller based on linear time-varying model predictive control (LTV-MPC) is proposed. Simulations are carried out to evaluate the control performance. The controller proposed in this paper is compared with an LTV-MPC-based feedback controller which models the lateral disturbance from gravity. It is shown that the controller proposed in this paper can achieve better control performance.

Recently, automatic modulation recognition (AMR), i.e., identifying the modulation modes of signals using deep learning (DL) has received much attention. This paper proposes an AMR method based on DL (i.e., SG-NET), including a novel DL architecture (i.e., GuResNet) based on the deep residual network (ResNet) and a SNR-aware mechanism, which can effectively extract signal characteristics to achieve a better recognition accuracy. Specifically, we design a deep residual network model that mainly consists of six novel Residual Units to abstract effective signal features and prevent over-fitting. Then, to further improve the recognition performance in low SNR scenarios (i.e., the SNR is lower than 0dB), we train different parameters in the GuResNets based on the aware SNR. That is, we firstly use original signals with high and low SNRs to train a general GusResNet. When SNR is larger than 0dB, we directly adopt the general GuResNet for AMR. While, in low SNR scenarios, we exploit the Legendre method to extract signal features and then re-train the parameters in the GuResNets under different SNR conditions to improve the recognition accuracy further. The simulations demonstrate that our proposed SG-NET can obtain nearly 30% accuracy gain when the SNR is lower than 0dB, and 10% improvement when SNR is larger than 0dB compared with existing schemes.

Communication-enabled Cooperative Adaptive Cruise Control (CACC) is an important technology to improve the safety and traffic capacity of vehicle platoons. However, most existing CACC research considers a conventional communication delay and fixed network topology. When the network is attacked, the stability of the system will be affected. In this paper, a CACC system considering dynamic network topologies and communication delay jitters is proposed. A multi-vehicle look ahead network topology of CACC is designed to investigate the relationship between communication delay and the optimal network topology. Control parameters are obtained by solving Algebraic Riccati equations. Moreover, the relationship between the minimum inter-vehicle spacing and the communication delay is analyzed and derived under different network topologies. Finally, the performance of the proposed CACC system with adaptive network topologies is evaluated by simulations. Simulation results demonstrate that the proposed CACC system outperforms the H infinity synthesis-based controller considering the fixed network topologies. The robustness against communication failure of the proposed system, such as the DeGrading of Service (DGoS) attack, can be significantly improved, which can reduce the minimum safety headway buffer and further support better mobility of vehicles.

High-definition map (HD Map) for autonomous driving brings huge pressure on networks due to its bandwidth-greedy, computing-intensive, and latency-sensitive characteristics. Data collection, transmission, and processing for HD Map update should cooperate to meet these requirements. In this paper, crowd-sensing which exploits the sensing ability of autonomous vehicles is adopted for real-time data collection. Vehicular distributed computing is adopted to improve the computing capability and reduce the transmission of massive raw environmental data. And a crowd-sensing assisted vehicular distributed computing (CS-VDC) mechanism is proposed based on the convergence of sensing, communication, and computation. In addition, considering the differences in sensing range and computing capability of different vehicles, the selection of crowd-sensing nodes and task allocation are jointly optimized to further minimize the communication load. A heuristic algorithm is developed to solve the optimization problem. The performance of the proposed mechanism is evaluated and CS-VDC can always achieve the minimum missing update ratio and amount of equivalent transmission data regardless of the parameter configuration. Especially, the amount of equivalent transmission data under the proposed CS-VDC can be reduced by 37% compared with the nearest node selection mechanism.

With the explosive growth of computation-intensive and latency-sensitive vehicular applications, limited on-board computing resources can hardly satisfy these heterogeneous requirements and task offloading becomes a potential solution. However, task offloading in vehicular networks may face the dilemma of unaffordable uploading time caused by the huge amount of uploading traffic. Therefore, considering the applications which use the environmental data as their input, the sensing abilities of serving nodes (SNs) are exploited and a traffic-aware task offloading (TATO) mechanism based on convergence of communication and sensing is proposed. In the TATO mechanism, a task vehicle can adaptively upload the input data to some SNs and transmit the computation instructions to others which use the environmental data sensed by themselves as input. The objective is to minimize the overall response time (ORT) by jointly optimizing the task and wireless bandwidth ratios. Next, a binary search and feasibility check (BSFC) algorithm is designed to solve the optimization problem. Simulation results demonstrate the effectiveness of the proposed BSFC algorithm and show that the TATO mechanism always outperforms the benchmark mechanisms (i.e., communication-based offloading and sensing-based offloading) in terms of the ORT. Specifically, when the task offloading traffic is huge and the wireless transmission capability becomes a bottleneck, TATO can reduce the ORT by 42.8% compared with that of the communication-based offloading.

Massive multiple-input multiple-output (MIMO) systems with 1-bit analog-to-digital converters (ADCs) are promising to reduce the energy consumption. However, the serious quantization error caused by 1-bit ADCs will potentially limit the feasibility of high order modulations. This paper focuses on the analysis of the high order phase-shift keying (PSK) signal transmission in the 1-bit ADC massive MIMO system. Firstly, assuming ideal channel estimation and a single mobile station (MS), we theoretically prove that with an asymptotically large number of antennas at the base station, PSK signals with arbitrary modulation order can be recovered in the 1-bit ADC massive MIMO system. Secondly, we analyze the impact of pilot based channel estimation on the recovery of the high order PSK signals, which leads to a periodic asymptotic detection phase error (ADPE) at high signal to noise ratio (SNR). Furthermore, we also propose to optimize the pilot sequence for minimizing the cumulative absolute ADPE. Finally, the analysis is extended to the multi-MS case and the performance with different pilot patterns is discussed. Simulation results validate our analysis and show that using our proposed optimized pilot sequence can significantly improve the detection performance for both single-MS and multi-MS 1-bit ADC massive MIMO systems.

In the fifth generation of mobile cellular network (5G), based on the allocating of communicating and computing resources, multiple slices are formed to serve various use cases with different quality of service (QoS) requirements. Since the communicating and computing resources are limited, it is important to share them among slices efficiently. However, the requirements on communicating and computing resources are usually coupled together considering mobile edge computing (MEC) in sliced 5G networks. To this end, this paper proposes the tandem queues to represent and analyze the coupling relationship between communicating and computing resources for slices. Then, based the derived relationship, the communicating and computing resources are allocated among slices when there is no burst computing task and there is burst computing task, which are formulated by two optimal problems. For the optimal problem without considering the burst computing task, the coupling relationship of the resources is introduced to the objective function to share the resources among the slices efficiently. Then considering the burst computing task, the probability that the enough resources are required by the slices is added to the constraint conditions. And two algorithms are designed to solve the two problems. By simulating, it can be seen that the system utility can be improved since more slices can be served by the system with the proposed algorithms. Therefore, the efficiency of managing communicating and computing resources jointly is proved.

Packet-based fronthaul (FH) transport networks are promising for future centralized radio access networks (C-RANs), which support statistical multiplexing via flow scheduling. With stringent requirements on FH delay, the FH network is time sensitive. Targeting to minimize the maximum FH delay of all packets, this paper investigates the optimized flow scheduling in the packet-based FH network. Due to the high complexity of optimal solutions, a heuristic higher rate flow scheduled later (HRSL) scheme is proposed to achieve good performance with a low complexity. The main idea is to schedule the higher-rated flow with a lower priority, because packets in the higher-rated flow have a larger solution space to be scheduled with a smaller delay. It is proved that using HRSL, each packet must have a transmission position and no packet will be discarded during scheduling. Moreover, the asymptotic delay of HRSL is derived when the number of flows is sufficiently large. We show that the delay performance of HRSL approaches to the optimal one and yields a reduction of up to 92.6% compared to existing schemes. The properties of HRSL are also verified via simulations. If the required delay is larger than the asymptotic delay performance, the FH network with HRSL can always deliver packets in time, no matter how many flows are scheduled and what the rates of flows are.

Mobile edge computing (MEC)-enabled vehicular networks can improve the quality of service (QoS) of vehicular networks, such as the round-trip time (RTT) and transmission control protocol (TCP) throughput. However, the high mobility of vehicles requires frequent service migrations among MEC servers to maintain the QoS. Frequent service migrations incur prohibitive migration cost. To achieve the tradeoff between the QoS and migration cost, this paper proposes a novel dynamic service migration scheme, which considers the effect of velocities of vehicles. The main idea is that the QoS and migration cost are modeled as the function of velocity, and then they are jointly considered economically. The system captures incomes from vehicles according to their QoS. The cost (expenditure) consists of the migration cost and service cost for the use of computing, communication and memory resources to provide service. The system utility is defined as the difference between incomes and costs. A novel deep reinforcement learning algorithm, i.e., deep Q-learning is employed to maximize the system utility, by designing dynamic service migration scheme. Simulation results show that compared with existing migration schemes, the proposed dynamic scheme can increase the system utility with various velocities. The system utility gain increases as the velocity increases, which reaches about 2 times when the velocity is larger than 30m/s. Moreover, it improves the QoS of vehicles with the higher mobility, where the RTT can be decreased by 2 times, and the TCP throughput can be increased by 1 time.

Next generation fronthaul interface (NGFI) is a promising fronthaul (FH) interface for the future centralized radio access network (C-RAN), which is packet-based and supports statistical multiplexing via flow scheduling at the node in the FH network. However, FH delay may be increased by flow scheduling. Targeting to minimize the maximum FH delay, this paper investigates the optimized flow scheduling in NGFI. Due to the high complexity of optimal solution, an intuitive low rate scheduled first (LRSF) scheme is proposed to achieve the good performance with low complexity. The main idea is to schedule the flow of lower rate with higher priority, so that the maximum FH delay of packets in the flow with lower priority can be reduced. Simulation results show that the delay performance of LRSF approaches to the optimal one and outperforms existing schemes.

In centralized radio access network (C-RAN), the statistical multiplexing gain (SMG) of computing resource can be achieved. This paper focuses on analyzing the SMG considering the quantization granularity of computing resource which indicates the degree of resource sharing in practical C-RAN. Firstly, based on a spatial-temporal traffic model for multiple cells, the quantized model of computing resource is set up. Then, giving a system service threshold and defining the SMG as the ratio of the amount of computing resources deployed in distributed radio access network (D-RAN) and CRAN, the asymptotic SMG with quantized computing resource is derived. The impacts of the service threshold, the granularity of quantized computing resource and the average traffic load on the asymptotic SMG are analyzed. In the special case that the granularity of quantized computing resource is infinitely small, i.e., the computing resources are not quantized, the asymptotic SMG only depends on the service threshold and the fluctuation of spatial traffic load. Simulations are carried out to verify the correctness of the derivation of the asymptotic SMG with quantized computing resource. Simulation results show that when the service threshold gets higher, the asymptotic SMG increases. Moreover, the asymptotic SMG decreases obviously when the ratio of the granularity of quantized computing resource to the average traffic load increases over 1%. In addition, compared to a C-RAN with dramatic traffic load fluctuation in spatial domain, a moderate fluctuation can bring a higher asymptotic SMG.

The mobile edge computing (MEC)-enabled cellular network provides a promising paradigm for emerging services with intensive computation and low delay requirement. However, when multiple computation-intensive and delay-sensitive services are required concurrently, how to minimize the delay and monetary cost on mobile devices (MDs) under the limited computation resource and communication resource remains a challenging issue. In order to solve the issue, we formulate computation offloading, communication resource and computation resource allocation as an optimization problem in MEC-enabled cellular network. The problem is non-convex. Hence, we formulate it as a multi-objective computation offloading and resource allocation (MCORA) game and prove the existence of Nash equilibrium (NE). To obtain the NE, we design a MCORA algorithm taking the uplink, downlink spectrum resource, computation resource and offloading strategy into consideration. Simulation results show the MCORA algorithm can minimize system cost of all MDs. Moreover, in comparison to the existing algorithms, the proposed algorithm can achieve better performance in delay and system cost.

In ultra-dense cellular networks (UDNs) with user/control plane (U/C) splitting, frequent handovers in user planes are unavoidable. This seriously degrades MS's transmission control protocol (TCP) throughput. This paper proposes a prediction-based user plane handover scheme to improve the TCP throughput in UDNs. Firstly, based on algorithms used in recommender systems, a mobility prediction algorithm called content-based collaborative hybrid filters (CCHF) is proposed to predict the target small base station (SBS). When the mobile station (MS) moves into the cell-edge of the source SBS, it can set up connections to the predicted target SBS and the source SBS simultaneously. An accurate prediction and a simultaneous connection can enhance the signal to interference and noise ratio (SINR) at cell-edge and reduce the handover interruption ratio (HIR). Thus packet loss can be reduced and the MS's TCP throughput will be improved. Simulations are carried out to verify the effectiveness of the proposed CCHF-handover. It is shown that using CCHF, the prediction accuracy of random trajectory can be improved by more than 100% compared with existing prediction algorithm. Moreover, the CCHF-handover improves the average TCP throughput significantly by more than 3 times compared with that of existing handover schemes.

In cache-enabled heterogeneous cloud radio access networks (HC-RANs), mobile station (MS) association for multimedia content delivery should consider both the content caching location and the wireless channel quality. This paper studies economically optimal MS association to tradeoff the cache-hit ratio and the ratio of MSs with satisfied quality of service (QoS). When the associated enhanced remote radio unit (eRRU) stores the requesting content, the content can be fetched directly from the local cache. Otherwise, fronthaul has to be used to fetch the content. The use of fronthaul resource and cache is treated as costs, and payments of QoS-satisfied MSs are treated as incomes. Thus, the economic MS association is formulated as an optimization problem to maximize the system utility, i.e., total profit of the network operator, which is defined as the difference between incomes and costs. A belief propagation-based method is employed to solve the problem on a developed factor graph. Simulation results show that the proposed economically optimal MS association achieves much higher profit than the existing schemes and works well in the network with various loads. Moreover, the profit of the proposed scheme can be improved with inter-cell interference coordination. For the case with extremely skewed content popularity, the proposed scheme can avoid MS overloading at eRRUs storing most popular multimedia contents. Furthermore, it can support more MSs with satisfied QoS, which leads to a higher profit.

We consider a heterogeneous ultra dense network (HUDN) with both the hexagon and Poisson point process (PPP) layouts, which is more relevant for practical scenarios. A user-centric and adaptive interference-aware non-coherent coordinated multi-point transmission (IA-CoMP) scheme is used as a system setup to reduce both the cross-tier and the co-tier inter-cell interference (ICI) for the HUDN with range expansion (RE) in small cells. Due to the involvement of hexagonal macrocells, it is intractable to analyze the coverage performance of HUDNs with IA-CoMP. To this end, we present a mobile station GrouPing (MSGP)-based coverage analysis method, which partitions all MSs into four groups according to their main interference, and the whole coverage is obtained as the sum of the coverage of each MS group. We demonstrate that the proposed MSGP-based coverage analysis method can provide a tight upper bound when compared with Monte Carlo simulations. As the small cell density increases, the system coverage of HUDNs with hexagonal macrocells reduces exponentially, while the system coverage of HUDNs with PPP-based macrocells remains unchanged. Moreover, the system coverage increases with the larger one of the main ICI judging coefficient and the RE bias.

The performance of cloud radio access networks (Cloud-RAN) is constrained by the limited fronthaul capacity. Physical layer caching is an effective technique to reduce the requirement on fronthaul capacity in peak time, which depends on the cache-hit ratio and the wireless transmission performance. Considering a dynamic points selection (DPS) based coordinated multi-point (CoMP) scheme to enhance the wireless transmission performance, this paper proposes an optimal probabilistic caching scheme to minimize the fronthaul capacity requirement in cloud-RAN. First of all, given the content caching probabilities, the average outage probability of cache-based services is derived. Based on the derivation, the relationship between the wireless transmission performance and the fronthaul capacity requirement is established. Then, an optimization problem is formulated to find an optimal probabilistic caching scheme to minimize the fronthaul capacity requirement, which can be solved using the interior point method. Simulation results show that using CoMP transmissions and optimizing the caching probabilities, the proposed scheme can reduce the fronthaul capacity requirement efficiently. Compared to the existing scheme which does not consider the effect of the wireless transmission performance on the fronthaul capacity requirement, the fronthaul capacity requirement can be reduced by 36\%. Moreover, the proposed scheme can also improve the cache service probability.

This paper concerns a heterogeneous ultra dense network (HUDN) with regularly deployed macrocells. Aiming to combat both cross-tier and co-tier inter-cell interference (ICI), this paper proposes a user-centric and adaptive interference aware coordinated multipoint transmission (IA-CoMP) scheme, which takes the serving or master base station (BS) and main interfering BSs decided by a signal strength threshold as cooperative nodes. The coverage performance of IA-CoMP is analyzed. The main idea is to divide MSs in HUDN into three types according to different main interference they suffer. Then the whole coverage can be obtained as the sum of the coverage of each type of MSs. A lower bound of MSs not using CoMP is derived for HUDN with IA-CoMP, which demonstrates that using IA-CoMP, unnecessary CoMP can be avoided and the overall system overhead can be reduced compared with that of the existing CoMP schemes with fixed size for all MSs. Simulations are carried out to verify the analysis and compare the coverage performance of IA-CoMP with that of cross-tier CoMP (CT-CoMP). It is shown that the proposed IA-CoMP scheme can provide a much better coverage performance especially in HUDN with higher small cell densities.

Computing resource allocation algorithms are very important in centralized radio access networks to reduce the power consumption and construction cost of base stations. Most of the current research cannot make the best use of computing resources because they take whole processing tasks of one BS as the allocation object which is too large for a processor. Therefore, an Energy Aware Dynamic Resource Allocation (EADRA) algorithm is proposed in this paper, which takes a whole BS or the sub-task of a BS as the allocation object dynamically to achieve the target that the processing ability of computing resources can be fully utilized. Simulation results show that the EADRA effectively decreases the number of allocated processor cores when compared with existing algorithms. In addition, EADRA reduces the power consumption more and more significantly compared to that of the existing algorithms when the number of Virtual BSs (VBSs) is gradually increased.

Ultra dense cellular networks (UDNs) are concerned in this paper, which employ Macro-Diversity- Coordinated Multipoint (MD-CoMP) to deal with the serious inter-cell interference. Considering the constraints of inter-cell resource allocation posed by CoMP and the difference of load in each cell, a joint clustering and inter-cell resource allocation is necessary. To achieve a feasible solution, the joint optimization is approximated by two sub-problems, i.e. clustering based on load information using game theory and inter-cell resource allocation based graph-coloring algorithms. Thus a two-step joint clustering and scheduling (TS-JCS) scheme is proposed. Simulation results illustrate that TS-JCS has the capability to jointly consider clustering with the requirements on resources. Compared to No-CoMP and the comparing scheme without consideration of resource allocation, TS-JCS can significantly improve the system performance, demonstrating the necessity and superiority of joint CoMP clustering and inter-cell resource allocation.

Recently, cognitive pilot channel (CPC) has been proposed to assist spectrum awareness and network selection in the heterogeneous network and dynamic spectrum access mechanism. The CPC channel can provide the necessary wireless environment information for the terminals and avoid long time- and energy-consumption for spectrum scanning. In previous researches, two different CPC information delivery modes have been proposed, i.e., broadcast CPC and on-demand CPC. In order to overcome the defects of these two approaches, Feng et al. proposed a multicast CPC method to optimize the delay performance and information scheduling as an evolution of the on-demand mode. However, in their mode, the waiting time of each mesh is fixed never mind the user distribution is high or not. In this paper, we analyze the relationship between waiting time and user distribution in different scenarios. And according to the difference among their user distribution, we propose setting an optimal waiting time for each mesh. While in actual systems, the mesh number usually is very large. Due to the computational complexity, it is difficult to get the optimal waiting time of each mesh. So we propose a clustering scheme as a suboptimal solution. This suboptimal solution especially fits for the case of centralized distribution scenario. Simulation results will be shown to demonstrate the performance of the optimal and suboptimal solutions.