Kezhi Wang

Joint Energy Minimization and Resource Allocation in C-RAN with Mobile Cloud

Cloud radio access network (C-RAN) has emerged as a potential candidate of the next generation access network technology to address the increasing mobile traffic, while mobile cloud computing (MCC) offers a prospective solution to the resource-limited mobile user in executing computation intensive tasks. Taking full advantages of above two cloud-based techniques, C-RAN with MCC are presented in this paper to enhance both performance and energy efficiencies. In particular, this paper studies the joint energy minimization and resource allocation in C-RAN with MCC under the time constraints of the given tasks. We first review the energy and time model of the computation and communication. Then, we formulate the joint energy minimization into a non-convex optimization with the constraints of task executing time, transmitting power, computation capacity and fronthaul data rates. This non-convex optimization is then reformulated into an equivalent convex problem based on weighted minimum mean square error (WMMSE). The iterative algorithm is finally given to deal with the joint resource allocation in C-RAN with mobile cloud. Simulation results confirm that the proposed energy minimization and resource allocation solution can improve the system performance and save energy.

Cost-effective resource allocation in C-RAN with mobile cloud

Taking full advantages of two cloud-based techniques, i.e., cloud radio access network (C-RAN) and mobile cloud computing (MCC), mobile operators will be able to provide the good service to the mobile user as well as increasing their revenue. This paper aims to minimize the mobile operators cost while at the same time, meet the task time constraints of the mobile users. In particular, we assume that the mobile cloud first completes the tasks for the mobile user and then transmits the results back to the users through C-RAN. Joint cost-effective resource allocation is proposed between MCC and C-RAN and simulation results confirm that the proposed cost minimization and resource allocation solution outperforms nonoptimal solutions.

BER and Optimal Power Allocation for Amplify-and-Forward Relaying Using Pilot-Aided Maximum Likelihood Estimation

Bit error rate (BER) and outage probability for amplify-and-forward (AF) relaying systems with two different channel estimation methods, disintegrated channel estimation and cascaded channel estimation, using pilot-aided maximum likelihood method in slowly fading Rayleigh channels are derived. Based on the BERs, the optimal values of pilot power under the total transmitting power constraints at the source and the optimal values of pilot power under the total transmitting power constraints at the relay are obtained, separately. Moreover, the optimal power allocation between the pilot power at the source, the pilot power at the relay, the data power at the source and the data power at the relay are obtained when their total transmitting power is fixed. Numerical results show that the derived BER expressions match with the simulation results. They also show that the proposed systems with optimal power allocation outperform the conventional systems without power allocation under the same other conditions. In some cases, the gain could be as large as several dBs in effective signal-to-noise ratio.

Outage Probability of Dual-Hop Selective AF With Randomly Distributed and Fixed Interferers

The outage probability performance of a dual-hop amplify-and-forward (AF) selective relaying system with global relay selection is analyzed for Nakagami- m fading channels in the presence of multiple interferers at both the relays and the destination. Two different cases are considered. In the first case, the interferers are assumed to have random number and locations. Outage probability using the generalized Gamma approximation (GGA) in the form of 1-D integral is derived. In the second case, the interferers are assumed to have fixed number and locations. Exact outage probability in the form of 1-D integral is derived. For both cases, closed-form expressions of lower bounds and asymptotic expressions for high signal-to-interference-plus-noise ratio (SINR) are also provided. Simplified closed-form expressions of outage probability for special cases (e.g., dominant interferences, independent identically distributed (i.i.d.) interferers, and Rayleigh distributed signals) are studied. Numerical results are presented to show the accuracy of our analysis by examining the effects of the number and locations of interferers on the outage performances of both AF systems with random or fixed interferers

Hard-Decision Fusion With Arbitrary Numbers of Bits for Different Samples

A new hard-decision fusion rule that combines arbitrary numbers of bits for different samples taken at different sensors is proposed. The best thresholds for the fusion rules using 2, 3, and 4 bits are obtained. The bit error rate for a hard-decision fusion rule with 1 bit is also derived. Numerical results show that the new scheme can achieve better performance with higher energy efficiency.

Statistics of

Exact results for the probability density function (PDF) and cumulative distribution function (CDF) of the sum of ratios of products (SRP) and the sum of products (SP) of independent α-μ random variables (RVs) are derived. They are in the form of 1-D integral based on the existing works on the products and ratios of α-μ RVs. In the derivation, generalized Gamma (GG) ratio approximation (GGRA) is proposed to approximate SRP. Gamma ratio approximation (GRA) is proposed to approximate SRP and the ratio of sums of products (RSP). GG approximation (GGA) and Gamma approximation (GA) are used to approximate SP. The proposed results of the SRP can be used to calculate the outage probability (OP) for wireless multihop relaying systems or multiple scattering channels with interference. The proposed results of the SP can be used to calculate the OP for these systems without interference. In addition, the proposed approximate result of the RSP can be used to calculate the OP of the signal-to-interference ratio (SIR) in a multiple scattering system with interference.

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Recently, commutating resource is playing an increasingly more important role in the user experience and future generation wireless networks, as exemplified by the recent progress in mobile edge computing (MEC) and cloud radio access networks (C-RAN). In this paper, by taking advantage of both C-RAN and MEC, we propose a novel mobile cloud-radio access network (MC-RAN) structure, which is composed of virtualized baseband unit (vBBU) as the communication computing providing unit (CCPU) and virtualized mobile clone (vMC) as the service computing providing unit (SCPU). In MC-RAN, both vBBU and vMC can be realized in mobile cloud in the form of cloud based virtute machines, which facilitate the dynamic computational resource allocation between CCPU and SCPU. In particular, this paper considers the power minimization computational resource allocation between CCPU and SCPU with the consideration of the quality of service (QoS) requirement of the user equipments (UEs). Simulation results confirm that the proposed power minimization and resource allocation solution can improve the system performance and save power.

Random Variables and Their Applications in Wireless Multihop Relaying and Multiple Scattering Channels

Energy detection is widely used in cognitive radio due to its low complexity. One fundamental challenge is that its performance degrades in the presence of noise uncertainty, which inevitably occurs in practical implementations. In this work, three novel detectors based on uniformly distributed noise uncertainty as the worst-case scenario are proposed. Numerical results show that the new detectors outperform the conventional energy detector with considerable performance gains. Copyright

Power-Minimization Computing Resource Allocation in Mobile Cloud-Radio Access Network

In this paper, we work on a Cache and Multi-layer MEC enabled C-RAN (CMM-CRAN) to handle various user tasks with minimized latency and energy cost. We intend to solve two particular problems of CMM-CRAN. First, because CMM-CRAN has to maximally cache the most frequently requested data from Service Provide Server (SPS) to Remote Radio Head (RRH) and later offered to proximity mobile users, the cache content placement from SPSs to RRHs becomes a many-to-many matching problem with peer effects. Second, because of multi-layer MEC, a user task has to be dynamically controlled to be offloaded to the best fit cloud, i.e., either local MEC or remote MEC, to get served. This dynamic task offloading is a Multi-Dimension Multiple-Choice Knapsack (MMCK) problem. To solve these two problems, we provide a Joint Cache content placement and task Offloading Solution (JCOS) to CMM-CRAN that utilizes Proportional Fairness (PF) as the user scheduling policy. JCOS applies a Gale-Shaply (GS) method to work out the cache content placement, and a Population Evolution (PE) game theory coupled with a use of Analytic Hierarchy Process(AHP) to work out the dynamic user task offloading. According to the simulation results, CMM-CRAN with JCOS is proved to be able to provide highly desired low-latency communication and computation services with decreased energy cost to mobile users.

ALRT-based energy detection using uniform noise distribution

Cloud radio access network (C-RAN) and mobile edge computing (MEC) have emerged as promising candidates for the next generation access network techniques. Unfortunately, although MEC tries to utilize the highly distributed computing resources in close proximity to user equipments equipments (UE), C-RAN suggests to centralize the baseband processing units (BBU) deployed in radio access networks. To better understand and address such a conflict, this paper closely investigates the MEC task offloading control in C-RAN environments. In particular, we focus on perspective of matching problem. Our model smartly captures the unique features in both MEC and C-RAN with respect to communication and computation efficiency constraints. We divide the cross-layer optimization into the following three stages: (1) matching between remote radio heads (RRH) and UEs, (2) matching between BBUs and UEs, and (3) matching between mobile clones (MC) and UEs. By applying the Gale-Shapley Matching Theory in the duplex matching framework, we propose a multi-stage heuristic to minimize the refusal rate for users task offloading requests. Trace-based simulation confirms that our solution can successfully achieve near-optimal performance in such a hybrid deployment.