When the wireless networks become large, it is hard to connect all base stations (BSs) to the core networks via the wired backhaul due to the increased infrastructure cost. Therefore, the integrated access and backhaul (IAB) networks, where the BS connects to the core networks via the wireless backhaul, has been emerged. In this paper, we consider a millimeter wave (mmWave) beamforming design and backhaul rate assignment in IAB networks. On top of this system model, we derive a closed form expression of the expected sum data rate. Moreover. we formulate the expected sum data rate maximization problem concerning the beamforming design and backhaul rate assignment. Since the formulated problem is a complex form and coupled with the optimization variables, we define a slack variable and divide the original problem into two subproblems. Then, we propose an iterative algorithm to obtain the optimal solution. Moreover, we provide a low-complex algorithm for large-scale networks by using a genetic algorithm (GA)-based approach. Finally, from numerical simulations, we show that the proposed solutions achieve a higher achievable sum data rate than the baseline schemes. We also explore the impacts of the bandwidth partitioning ratio, the biased factor ratio, the density of the user and the number of macro base station (MBS)’s transmit antennas on the achievable sum data rate.

In this paper, we consider the service caching and the computing resource allocation in edge computing (EC) enabled networks. We introduce a random service caching design considering multiple types of latency sensitive services and the base stations (BSs)’ service caching storage. We then derive a successful service probability (SSP). We also formulate a SSP maximization problem subject to the service caching distribution and the computing resource allocation. Then, we show that the optimization problem is nonconvex and develop a novel algorithm to obtain the stationary point of the SSP maximization problem by adopting the parallel successive convex approximation (SCA). Moreover, to further reduce the computational complexity, we also provide a low complex algorithm that can obtain the near-optimal solution of the SSP maximization problem in high computing capability region. Finally, from numerical simulations, we show that proposed solutions achieve higher SSP than baseline schemes. Moreover, we show that the near-optimal solution achieves reliable performance in the high computing capability region. We also explore the impacts of target delays, a BSs’ service cache size, and an EC servers’ computing capability on the SSP.

In this paper, we consider beamforming vector allocation and backhaul link capacity allocation in millimeter wave (mmWave) integrated access and backhaul (IAB) networks. We consider out-of-band backhaul and each base station (BS) has a Line-of-Sight (LoS) probability. We then derive a sum ergodic capacity using stochastic geometry. We also formulate the sum ergodic capacity maximization problem subject to the beamforming vector and backhaul link capacity allocations of the macro base station (MBS). Then, we show the optimization problem is a convex problem, and hence, we obtain a global optimal solution for our optimization problem using the Karush-Kuhn-Tucker (KKT) condition. Finally, from numerical simulation, we show that the proposed solutions achieve higher sum ergodic capacity than baseline schemes. We also explore the impacts of a backhaul-access bandwidth partitioning ratio and a density of small base stations (SBSs).

Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, inappropriate task offloading to roadside units (RSUs) can lead to large energy consumption, which will result in negative economic, environmental, and performance impacts. Therefore, in this paper, we develop the energy-efficient cooperative offloading scheme for edge computing-enabled vehicular networks. We first establish a novel cooperative offloading model to multiple RSUs for given batch of moving vehicles, different from existing works that consider single vehicle only or static users. Then, we consider the total energy minimization by optimizing the task splitting ratio, computation resource, and communication resource, which is a challenging non-convex problem, and provide optimal solutions for multi-vehicle case and single-vehicle case, respectively. Furthermore, we extend our proposed scheme to the one for a more realistic scenario (i.e., online scenario), where batches of vehicles sequentially approach the RSUs. Finally, through numerical results, the impact of network parameters on the total energy consumption is analyzed, and we verify that our proposed solution consumes lower energy than baseline schemes.

In this paper, we consider a large-scale edge computing (EC)-enabled network. We consider multiple latency-sensitive services. We adopt a random service caching scheme and a computation resource allocation scheme at base stations (BSs). We first derive the successful service probability (SSP). Using tools from stochastic geometry and queuing theory, we formulate the SSP maximization problem with respect to (w.r.t.) the service caching distribution and computation resource allocation, which is a challenging non-convex problem due to the complicated form of the SSP. Using parallel successive convex approximation (SCA), we develop an efficient iterative algorithm to obtain a stationary point of the non-convex problem. Finally, by numerical simulations, we show that the proposed solution achieves a higher SSP than the baseline schemes. We also show the impacts of the cache size and service rate of EC servers.

In this paper, we propose a novel coordinated multipoint (CoMP) downlink transmission strategy for the ultra-dense network (UDN) environment. In this proposed CoMP transmission scheme, we exploit the average received link power (ARLP) of the base stations (BSs) with respect to the ARLP of the strongest BS to a typical user, to dynamically adjust the number of cooperating BSs serving that user in the network. This allows us to effectively manage the inter-cell interference in the UDN regime. Our numerical results and simulations show that the proposed scheme can out-perform the existing fixed number of BS based CoMP transmission scheme in the high BS density scenario.