In this report, we introduce the concept of Rician K-factor-based radio resource and mobility management for fifth generation (5G) ultra-dense networks (UDN), where the information on the gradual visibility between the new radio node B (gNB) and the user equipment (UE)-dubbed X-line-of-sight (XLOS)-would be required. We therefore start by presenting the XLOS service probability as a new performance indicator; taking into account both the UE serving and neighbor cells. By relying on a lognormal K-factor model, a closed-form expression of the XLOS service probability in a 5G outdoor UDN is derived in terms of the multivariate Fox H-function; wherefore we develop a GPU-enabled MATLAB routine and automate the definition of the underlying Mellin-Barnes contour via linear optimization. Residue theory is then applied to infer the relevant asymptotic behavior and show its practical implications. Finally, numerical results are provided for various network configurations, and underpinned by extensive Monte-Carlo simulations. output = ones(size(s(1,:))); 49 else 50 L1 = size(s,1); 51 comb = 0; 52 for i = 1 : L1 53 [pp ss] = meshgrid(p,s(i,:)); 54 mm = meshgrid(m(i,:),s(i,:)); 55 comb = comb + mm . * ss; 56 end 57 output = reshape(prod(gammas(pp + comb),2),size(s(1,:))); 58 end 59 end 60 end 61 % gammas function here is the complex gamma, available in 62 % www.mathworks.com/matlabcentral/fileexchange/3572-gamma 63 end APPENDIX D AUTOMATIC CONTOUR GENERATOR 1 function c = mfoxcontour(W, dim, an, Alphan, varargin) 2 % an = [a1,...,an] and Alphan = [alpha,1,1 ... alpha,n,1;...; alpha,1,r...alpha,n,r] 3 % varargin form (i = 1 ..r): [ci,1 ...ci,n ; gammai,1 ... gammai,n], 4 % [di,1 ...di,m ; deltai,1 ... deltai,m] 5 % See notation in A. Mathai, The H-function, Theory and Applications, Annex A.1 6 % W : control the width of the integration interval in [-i\infty +i\infty] 7 % dim : stands for the dimension 8 9 Nvar = length(varargin); 10 epsilon = 1/10; 11 f = ones(1,dim); 12 Q = -Alphan.'; 13 b = 1-an-epsilon; 14 lb = []; 15 ub = []; 16 17 for i = 1 : Nvar/2 18 cni = cell2mat(varargin(2 * (i-1)+1)); % [c1,i ...cn,i;gamma_1,i...gamma_n,i] 19 if(isempty(cni)) cni = [-1e10;1]; end 20 dmi = cell2mat(varargin(2 * (i-1)+2)); % [d1,i ...dm,i;delta_1,i...delta_m,i] 21 if(isempty(dmi)) dmi = [1e10;1]; end
Most of the well-known fading distributions, if not all of them, could be encompassed by the Fox's H-function fading. Consequently, we investigate the physical layer security (PLS) over Fox's H-function fading wiretap channels, in the presence of non-colluding and colluding eavesdroppers. In particular, for the non-colluding scenario, closed-form expressions are derived for the secrecy outage probability (SOP), the probability of nonzero secrecy capacity (PNZ), and the average secrecy capacity (ASC). These expressions are given in terms of either univariate or bivariate Fox's H-function. In order to show the effectiveness of our derivations, three metrics are respectively listed over the following frequently used fading channels, including Rayleigh, Weibull, Nakagami-m, α − µ, Fisher-Snedecor (F-S) F , and extended generalized-K (EGK). Our tractable results are not only straightforward and general, but also feasible and applicable, especially the SOP, which is usually limited to the lower bound in the literature due to the difficulty of deriving closed-from analytical expressions. For the colluding scenario, a super eavesdropper equipped with maximal ratio combining (MRC) or selection-combining (SC) schemes is characterized. The lower bound of SOP and exact PNZ are thereafter derived with closed-form expressions in terms of the multivariate Fox's H-function. In order to validate the accuracy of our analytical results, Monte-Carlo simulations are subsequently conducted for the aforementioned fading channels. One can observe that for the former non-colluding scenario, we have perfect agreement between the exact analytical and simulation results, and highly accurate approximations between the exact and asymptotic analytical results. On the contrary, the SOP and PNZ of colluding eavesdropper is greatly degraded with the increase of the number of eavesdroppers. Also, the so-called super eavesdropper with MRC is much powerful to wiretap the main channel than the one with SC.Index Terms-Physical layer security, Fox's H-function wiretap fading channels, Mellin transform, secrecy outage probability, probability of non-zero secrecy capacity, average secrecy capacity.
Abstract-In this letter, we present an end-to-end performance analysis of dual-hop project-and-forward relaying in a realistic scenario, where the source-relay and the relay-destination links are experiencing MIMO-pinhole and Rayleigh channel conditions, respectively. We derive the probability density function of both the relay post-processing and the end-to-end signal-to-noise ratios, and the obtained expressions are used to derive the outage probability of the analyzed system as well as its end-to-end ergodic capacity in terms of generalized functions. Applying then the residue theory to Mellin-Barnes integrals, we infer the system asymptotic behavior for different channel parameters. As the bivariate Meijer-G function is involved in the analysis, we propose a new and fast MATLAB implementation enabling an automated definition of the complex integration contour. Extensive Monte-Carlo simulations are invoked to corroborate the analytical results.
In this paper, we address the issue of resource provisioning as an enabler for end-to-end dynamic slicing in software defined networking/network function virtualization (SDN/NFV)based fifth generation (5G) networks. The different slices' tenants (i.e. logical operators) are dynamically allocated isolated portions of physical resource blocks (PRBs), baseband processing resources, backhaul capacity as well as data forwarding elements (DFE) and SDN controller connections. By invoking massive key performance indicators (KPIs) datasets stemming from a live cellular network endowed with traffic probes, we first introduce a low-complexity slices' traffics predictor based on a soft gated recurrent unit (GRU). We then build-at each virtual network function-joint multi-slice deep neural networks (DNNs) and train them to estimate the required resources based on the traffic per slice, while not violating two service level agreement (SLA), namely, violation rate-based SLA and resource bounds-based SLA. This is achieved by integrating dataset-dependent generalized non-convex constraints into the DNN offline optimization tasks that are solved via a non-zero sum two-player game strategy. In this respect, we highlight the role of the underlying hyperparameters in the trade-off between overprovisioning and slices' isolation. Finally, using reliability theory, we provide a closed-form analysis for the lower bound of the so-called reliable convergence probability and showcase the effect of the violation rate on it.
Network slicing is a powerful tool to harness the full potential of fifth-generation (5G) systems. It allows verticals to own and exploit independent logical networks on top of the same physical infrastructure. Motivated by the emergence of the big data paradigm, this paper focuses on the enablers of big databased intelligent network slicing. The article starts by revisiting the architecture of this technology that consists of data collection, storage, processing, and analytics before it highlights their relationship with network slicing concepts and the underlying tradeoffs. It then proposes a complete framework for implementing big data-driven dynamic slicing resource provisioning while respecting service level agreements (SLAs). This includes the development of low-complexity slices' traffics predictors, resource allocation models and SLA enforcement via constrained deep learning. The paper finally identifies the key challenges and open research directions in this emerging area.
Artificial intelligence (AI)-driven zero-touch network slicing (NS) is a new paradigm enabling the automation of resource management and orchestration (MANO) in multi-tenant beyond 5G (B5G) networks. In this paper, we tackle the problem of cloud-RAN (C-RAN) joint slice admission control and resource allocation by first formulating it as a Markov decision process (MDP). We then invoke an advanced continuous deep reinforcement learning (DRL) method called twin delayed deep deterministic policy gradient (TD3) to solve it. In this intent, we introduce a multi-objective approach to make the central unit (CU) learn how to re-configure computing resources autonomously while minimizing latency, energy consumption and virtual network function (VNF) instantiation cost for each slice. Moreover, we build a complete 5G C-RAN network slicing environment using OpenAI Gym toolkit where, thanks to its standardized interface, it can be easily tested with different DRL schemes. Finally, we present extensive experimental results to showcase the gain of TD3 as well as the adopted multi-objective strategy in terms of achieved slice admission success rate, latency, energy saving and CPU utilization.
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