Wired-wireless convergence technology such as radio-over-fiber (RoF) is regarded as a promising candidate to deliver broadband wireless data from/to edge cloud to/from antenna sites, a notable ...example being the fronthaul (FH) in centralized/cloud radio access network (C-RAN). In 5G and beyond, the latency, bandwidth and fidelity requirements on FH pose great challenges to the RoF schemes. In this article, we focus on analog-to-digital-compression RoF (ADX-RoF) scheme based on low-latency MIMO data compression, which has the potential to reduce FH data rate by ∼90% compared with traditional digital RoF (CPRI) while still maintaining signal fidelity for high-order radio modulation. To verify the practicality of ADX-RoF in the latency-sensitive FH scenario, real-time hardware demonstration with latency evaluation is indispensable. We propose and design a real-time ADX, which achieves low-latency and high-throughput together with high-fidelity. Enabled by the ADX prototyped on a single-chip field-programmable radio platform (i.e., Xilinx RFSoC), we experimentally demonstrate 16-channel MIMO radio reception, real-time ADX processing and RoF transport. With <500 ns one-way ADX latency, less than 1.5% average EVM is achieved for 1024QAM, 61.44 MHz NR-class signals at a compression ratio of 13.3%. Negligible fiber-induced performance penalty is observed after 40 km transport. The results highlight the attractiveness of ADX-RoF for future FH.
With the increasing number of Internet of Things (IoT) devices, a huge amount of latency-sensitive and computation-intensive IoT applications have been injected into the network. Deploying mobile ...edge computing (MEC) servers in cloud radio access network (C-RAN) is a promising candidate, which brings a number of critical IoT applications to the edge network, to reduce the heavy traffic load and the end-to-end latency. The MEC server's deployment mechanism is highly related to the user allocation. Therefore, in this paper, we study hierarchical deployment of MEC servers and user allocation problem. We first formulate the problem as a mixed integer nonlinear programming (MINLP) model to minimize the deployment cost and average latency. In terms of the MINLP model, we then propose an enumeration algorithm and approximate algorithm based on the improved entropy weight and TOPSIS methods. Numerical results show that the proposed algorithms can reduce the total cost, and the approximate algorithm has lower total cost comparing the heaviest-location first and the latency-based algorithms.
In this paper, we investigate the potential benefits of the massive multiple-input multiple-output (MIMO) enabled heterogeneous cloud radio access network (C-RAN) in terms of the secrecy and energy ...efficiency (EE). In this network, both remote radio heads (RRHs) and massive MIMO macrocell base stations are deployed and soft fractional frequency reuse is adopted to mitigate the intertier interference. We first examine the physical layer security by deriving the area ergodic secrecy rate and secrecy outage probability. Our results reveal that the use of massive MIMO and C-RAN can greatly improve the secrecy performance. For C-RAN, a large number of RRHs achieves high area ergodic secrecy rate and low-secrecy outage probability, due to its powerful interference management. We find that for massive MIMO aided macrocells, having more antennas and serving more users improves secrecy performance. Then, we derive the EE of the heterogeneous C-RAN, illustrating that increasing the number of RRHs significantly enhances the network EE. Furthermore, it is indicated that allocating more radio resources to the RRHs can linearly increase the EE of RRH tier and improve the network EE without affecting the EE of the macrocells.
This paper studies the energy efficiency of the cloud radio access network (C-RAN), specifically focusing on two fundamental and different downlink transmission strategies, namely the data-sharing ...strategy and the compression strategy. In the data-sharing strategy, the backhaul links connecting the central processor (CP) and the base-stations (BSs) are used to carry user messages-each user's messages are sent to multiple BSs; the BSs locally form the beamforming vectors then cooperatively transmit the messages to the user. In the compression strategy, the user messages are precoded centrally at the CP, which forwards a compressed version of the analog beamformed signals to the BSs for cooperative transmission. This paper compares the energy efficiencies of the two strategies by formulating an optimization problem of minimizing the total network power consumption subject to user target rate constraints, where the total network power includes the BS transmission power, BS activation power, and load-dependent backhaul power. To tackle the discrete and nonconvex nature of the optimization problems, we utilize the techniques of reweighted ℓ 1 minimization and successive convex approximation to devise provably convergent algorithms. Our main finding is that both the optimized data-sharing and compression strategies in C-RAN achieve much higher energy efficiency as compared to the nonoptimized coordinated multipoint transmission, but their comparative effectiveness in energy saving depends on the user target rate. At low user target rate, data-sharing consumes less total power than compression; however, as the user target rate increases, the backhaul power consumption for data-sharing increases significantly leading to better energy efficiency of compression at the high user rate regime.
This paper considers a content-centric fog radio access network (F-RAN). Its multi-antenna remote radio heads (RRHs) are capable of caching and executing signal processing for content delivery to its ...users. The fronthaul traffic is thus saved since its baseband processing unit (BBU) needs to transfer only the cache-missed content items to the RRHs via limited-capacity fronthaul links. The problem of beamforming design maximizing the energy efficiency in content delivery subject to the quality-of-content-service constraints in terms of content throughput and fronthaul limited-capacity is addressed. Unlike the user's throughput in user-centric networks, the content throughput in content-centric networks is no longer a differentiable function of the beamforming vectors. The problem is inherently high-dimensional due to the involvement of many beamforming vectors even in simple cases of three RRHs serving three users. Path-following algorithms, which invoke a simple convex quadratic optimization problem to generate a better feasible point, are proposed for computation of this nonsmooth and high-dimensional optimization problem. We also employ generalized zero-forcing beamforming, which forces the multi-content interference to zero or nearly to zero to reduce the problem dimensionality for computational efficiency. Numerical results are provided to demonstrate their computational effectiveness. They also reveal that when the fronthaul traffic becomes more flexible, hard-transfer fronthauling is more energy efficient than soft-transfer fronthauling.
By migrating baseband processing functionalities into a centralized cloud-based baseband unit (BBU) pool, cloud radio access network (C-RAN) facilitates cooperative transmission among remote radio ...heads (RRHs) and enables flexible computation provisioning in the BBU pool. In C-RAN, due to the high amount of data transfer from the BBU pool to RRHs through fronthauls, limited fronthaul capacity becomes a key factor when designing cooperative transmission schemes among RRHs. Meanwhile, as computational resources are provisioned to mobile users (MUs) for baseband processing in the form of virtual machines (VMs) in the BBU pool, an effective VM assignment strategy is also with great significance. In this paper, we propose a holistic framework for green C-RAN under the constraint of limited fronthaul capacity, where we jointly optimize hybrid clustering and computation provisioning to appropriately provide a cluster of RRHs and a VM to each MU for cooperative transmission and baseband processing, aiming at minimizing the system power consumption. The system power minimization problem is formulated as an integer non-linear programming problem, which is hard to tackle. For tractability purpose, we transform this problem to an equivalent hybrid clustering problem embedded with a series of VM assignment problems. On this basis, we first achieve the optimal solution for system power minimization with high computational complexity, and then, a greedy algorithm is proposed to solve the hybrid clustering problem for practical implementation. Finally, the simulation results demonstrate that the proposed joint optimization of hybrid clustering and computation provisioning can significantly reduce the system power consumption.
A multi-user fog radio access network (F-RAN) is designed for supporting content-centric services. The requested contents are partitioned into sub-contents, which are then 'beamformed' by the remote ...radio heads (RRHs) for transmission to the users. Since a large number of beamformers must be designed, this poses a computational challenge. We tackle this challenge by proposing a new class of regularized zero forcing beamforming (RZFB) for directly mitigating the inter-content interferences, while the 'intra-content interference' is mitigated by successive interference cancellation at the user end. Thus each beamformer is decided by a single real variable (for proper Gaussian signaling) or by a pair of complex variables (for improper Gaussian signaling). Hence the total number of decision variables is substantially reduced to facilitate tractable computation. To address the problem of energy efficiency optimization subject to multiple constraints, such as individual user-rate requirement and the fronthauling constraint of the links between the RRHs and the centralized baseband signal processing unit, as well as the total transmit power budget, we develop low-complexity path-following algorithms. Finally, we confirm their performance by simulations.
In this letter, a framework to optimize device association (DA), radio resource allocation (RA), and power allocation (PA) in heterogeneous cloud radio access network (H-CRAN) is proposed. In H-CRAN, ...small remote radio heads (SRRHs) are deployed underlaying the macro remote radio head (MRRH) to serve a group of SRRH devices (SDVs) by reusing the same radio resources allocated to MRRH devices (MDVs). The DA, RA, and PA optimization process is designed to maximize the network sum-rate while guaranteeing the rate requirements for SDVs, fronthaul capacity constraints, and MDVs interference protection. For DA and RA, an approach dependent on matching game is proposed to model the interactions between devices and SRRHs. In addition, Lagrangian dual decomposition is adopted to determine the optimum power allocation for each radio resource. Simulation results validate the superiority of the proposed algorithm compared with other schemes.
5G mobile networks are envisioned to substantiate new vertical services with diverse performance requirements. Slicing in the radio access network (RAN) promises an efficient solution for these ...diversified needs of 5G networks, which foresees the separation of the base station functionality between the central unit (CU) and the distributed remote radio heads. In this article, we formulate a mixed integer programming (MIP) framework that maximizes the throughput by jointly selecting the optimal functional split and the routing path from a connected user equipment to the CU, while satisfying the agreed service level agreements (SLAs) of each service. Furthermore, we propose an effective heuristic, SlicedRAN, which creates isolated RAN slices premised on the service requirements connected through a fronthaul/backhaul (FH/BH) network and obtains near-optimal solutions in a short computing time compared to the MIP framework. Our results show that there is a tradeoff between the architecture of the FH/BH network and the minimum SLA of each slice, which provides a solution to efficiently design a virtualized network infrastructure. According to the results, the SlicedRAN outperforms existing state-of-the-art up to 112% gain in throughput. Results are shown close to the optimal results, with a loss below 5%.
Narrowband Internet of Things (NB-IoT) is one of the most promising technologies for enabling reliable communication among low-power, and low cost devices present in massive machine-type ...communications (mMTC). In NB-IoT, random access (RA) is implemented in the medium access control (MAC) layer to resolve access contention among massive IoT devices. Efficient network access techniques are required to effectively solve the massive access issues in NB-IoT, guaranteeing increased throughput and high spectrum utilization. In this paper, we present a comprehensive overview of NB-IoT towards supporting mMTC, with focus on the NB-IoT coexistence with 5G, as well the design challenges and requirements of RA in NB-IoT. Moreover, available literature is reviewed to highlight the RA congestion control schemes proposed during the past few years to alleviate RA collisions. While existing RA approaches mainly focus on conventional contention-based techniques for performing RA, intelligent learning based and grant-free Non-Orthogonal Multiple Access (NOMA) have been identified as a potential candidates to increase the transmission efficiency of mMTC applications.
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