Delay distributions of slotted ALOHA and CSMA

We derive the closed-form delay distributions of slotted ALOHA and nonpersistent carrier sense multiple access (CSMA) protocols under steady state. Three retransmission policies are analyzed. We find that under a binary exponential backoff retransmission policy, finite average delay and finite delay variance can be guaranteed for G<2S and G<4S/3, respectively, where G is the channel traffic and S is the channel throughput. As an example, in slotted ALOHA, S<(ln2)/2 and S<3(ln4-ln3)/4 are the operating ranges for finite first and second delay moments. In addition, the blocking probability and delay performance as a function of r/sub max/ (maximum number of retransmissions allowed) is also derived

Maximally Flexible Assignment of Orthogonal Variable Spreading Factor Codes for Multi-Rate Traffic

In universal terrestrial radio access (UTRA) systems, orthogonal variable spreading factor (OVSF) codes are used to support different transmission rates for different users. In this paper, we first define the flexibility index to measure the capability of an assignable code set in supporting multirate traffic classes. Based on this index, two single-code assignment schemes, nonrearrangeable and rearrangeable compact assignments, are proposed. Both schemes can offer maximal flexibility for the resulting code tree after each code assignment. We then present an analytical model and derive the call blocking probability, system throughput and fairness index. Analytical and simulation results show that the proposed schemes are efficient, stable and fair

6G Network AI Architecture for Everyone-Centric Customized Services

Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions

Performance analysis of borrowing with directional carrier locking strategy in cellular radio systems

A new carrier based dynamic channel assignment for FDMA/TDMA cellular systems, called borrowing with directional carrier locking strategy, is proposed in this paper. When a call arrives at a cell and finds all voice channels busy, a carrier which consists of multiple voice channels can be borrowed from its neighboring cells for carrying the new call if such borrowing will not violate the cochannel interference constraint. Two analytical models, cell group decoupling analysis and phantom cell analysis, are constructed for evaluating the performance of the proposed strategy. Using cell group decoupling (CGD) analysis, a cell is decoupled together with its neighbors from the rest of the network for finding its call blocking probability. Unlike conventional approaches, decoupling enables the analysis to be confined to a local/small problem size and thus efficient solution can be found. For a planar cellular system with three-cell channel reuse pattern, using CGD analysis involves solving of seven-dimensional Markov chains. It becomes less efficient as the number of carriers assigned to each cell increases. To tackle this, we adopt the phantom cell analysis which can simplify the seven-dimensional Markov chain to two three-dimensional Markov chains. Using phantom cell analysis for finding the call blocking probability of a cell, two phantom cells are used to represent its six neighbors. Based on extensive numerical results, we show that the proposed strategy is very efficient in sharing resources among base stations. For low to medium traffic loads and small number of voice channels per carrier, we show that both analytical models provide accurate prediction on the system call blocking probability.link_to_subscribed_fulltex

Phantom cell analysis of dynamic channel assignment in cellular mobile systems

In this paper, we propose the phantom cell analysis for dynamic channel assignment. This is an approximate analysis that can handle realistic planar systems with the threecell channel-reuse pattern. To find the blocking probability of a particular cell, two phantom cells are used to represent its six neighboring cells. Then, by conditioning on the relative positions of the two phantom cells, the blocking probability of that particular cell can be found. We found that the phantom cell analysis is not only very accurate in predicting the blocking performance, but also very computationally efficient. Besides, it is applicable to any traffic and channel-reuse patterns. © 1998 IEEE.link_to_subscribed_fulltex

Node placement optimization in ShuffleNets

Node placement problem in ShuffleNets is a combinatorial optimization problem. In this paper, an efficient node placement algorithm called the Gradient Algorithm is proposed. A communication cost function between a node pair is defined and the Gradient Algorithm places the node pairs one by one based on the gradient of the cost function. Then two lower bounds on the traffic weighted mean internodal distance h̄ are proposed. The performance of the Gradient Algorithm is compared to the lower bounds as well as some algorithms in the literature. Significant reduction of h̄ is obtained with the use of the Gradient Algorithm, especially for highly skewed traffic distributions. For a ShuffleNet with N = 64 nodes, the h̄ found is only 22% above the lower bound for the uniform random traffic distribution, and 14.7% for a highly skewed traffic distribution with skew factor γ = 100.link_to_subscribed_fulltex

Fixed channel assignment optimization for cellular mobile networks

The optimization of channel assignment in cellular mobile networks is an NP-complete combinatorial optimization problem. For any reasonable size network, only sub-optimal solutions can be obtained by heuristic algorithms. In this paper, six channel assignment heuristic algorithms are proposed and evaluated. They are the combinations of three channel assignment strategies and two cell ordering methods. What we found are (i) the node-color ordering of cells is a more efficient ordering method than the node-degree ordering; (ii) the frequency exhaustive strategy is more suitable for systems with highly non-uniformly distributed traffic, and the requirement exhaustive strategy is more suitable for systems with less nonuniformly distributed traffic; and (iii) the combined frequency and requirement exhaustive strategy with node-color re-ordering is the most efficient algorithm. The frequency spans obtained using the proposed algorithms are much lower than that reported in the literature, and in many cases are equal to the theoretical lower bounds.link_to_subscribed_fulltex

Node placement optimization in ShuffleNets

Node placement problem in ShuffleNets is a combinatorial optimization problem. In this paper an efficient node placement algorithm, called the gradient algorithm, is proposed. A communication cost function between a node pair is defined and the gradient algorithm places the node pairs one by one, based on the gradient of the cost function. Then two lower bounds on the traffic weighted mean internodal distance h are proposed. The performance of the gradient algorithm is compared to the lower bounds as well as to some algorithms in the literature. Significant reduction of h̄ is obtained with the use of the gradient algorithm, especially for highly skewed traffic distributions. For a ShuffleNet with N = 64 nodes, the h̄ found is only 22% above the lower bound for the uniform random traffic distribution, and 14.7% for a highly skewed traffic distribution with skew factor γ = 100. © 1998 IEEE.link_to_subscribed_fulltex