An efficient evolutionary algorithm for channel resource managementin cellular mobile systems

Modern cellular mobile communications systems are characterized by a high degree of capacity. Consequently, they have to serve the maximum possible number of calls while the number of channels per cell is limited. The objective of channel allocation is to assign a required number of channels to each cell such that both efficient frequency spectrum utilization is provided and interference effects are minimized. Channel assignment is therefore an important operation of resource management and its efficient implementation increases the fidelity, capacity, and quality of service of cellular systems. Most channel allocation strategies are based on deterministic methods, however, which result in implementation complexity that is prohibitive for the traffic demand envisaged for the next generation of mobile systems. An efficient heuristic technique capable of handling channel allocation problems is introduced as an alternative. The method is called a combinatorial evolution strategy (CES) and belongs to the general heuristic optimization techniques known as evolutionary algorithms (EAs). Three alternative allocation schemes operating deterministically, namely the dynamic channel assignment (DCA), the hybrid channel assignment (HCA), and the borrowing channel assignment (BCA), are formulated as combinatorial optimization problems for which CES is applicable. Simulations for representative cellular models show the ability of this heuristic to yield sufficient solutions. These results will encourage the use of this method for the development of a heuristic channel allocation controller capable of coping with the traffic and spectrum management demands for the proper operation of the next generation of cellular systems     

Using Cellular Probabilistic Self-Organizing Map in Borrowing Channel Assignment for Patterned Traffic Load

The fast growing cellular mobile systems demand more efficient and faster channel allocation techniques. Borrowing channel assignment (BCA) is a compromising technique between fixed channel allocation (FCA) and dynamic channel allocation (DCA). However, in the case of patterned traffic load, BCA is not efficient to further enhance the performance because some heavy-traffic cells are unable to borrow channels from neighboring cells that do not have unused nominal channels. The performance of the whole system can be raised if the short-term traffic load can be predicted and the nominal channels can be re-assigned for all cells. This paper describes an improved BCA scheme using traffic load prediction. The prediction is obtained by using the short-term forecasting ability of cellular probabilistic self-organizing map (CPSOM). This paper shows that the proposed CPSOM-based BCA method is able to enhance the performance of patterned traffic load compared with the traditional BCA methods. Simulation results corroborate that the proposed method delivers significantly better performance than BCA for patterned traffic load situations, and is virtually as good as BCA in the other situations analyzed.     

Improvement of borrowing channel assignment for patterned traffic load by online cellular probabilistic self-organizing map

This paper describes an improvement of borrowing channel assignment (BCA) for patterned traffic load by using the short-term traffic prediction ability of cellular probabilistic self-organizing map (CPSOM). The fast growing cellular mobile systems demand more efficient and faster channel allocation techniques today. In case of patterned traffic load, the traditional BCA methods are not efficient to further enhance the performance because heavy-traffic cells cannot borrow channels from their neighboring cells with light or medium traffic that may have unused nominal channels. The performance can be increased if the short-term traffic load can be predicted. The predicted results can then be used for channel re-assignment. Therefore, the unused nominal channels of the light-or-medium-traffic cells can be transferred to the heavy-traffic cells that need more nominal channels. In this paper, CPSOM is used online for traffic prediction. In this sense, the proposed CPSOM-based BCA method is able to enhance the performance for patterned traffic load compared with the traditional BCA methods. Simulation results corroborate that the proposed method enables the system to work with better performance for patterned traffic load than the traditional BCA methods.     

Performance analysis of a threshold based distributed channel allocation algorithm for cellular networks

Rapid advances of the handheld devices and the emergence of the demanding wireless applications require the cellular networks to support the demanding user needs more effectively. The cellular networks are expected to provide these services under a limited bandwidth. Efficient management of the wireless channels by effective channel allocation algorithms is crucial for the performance of any cellular system. To provide a better channel usage performance, dynamic channel allocation schemes have been proposed. Among these schemes, distributed dynamic channel allocation approaches showed good performance results. The two important issues that must be carefully addressed in such algorithms are the efficient co-channel interference avoidance and messaging overhead reduction. In this paper, we focus on our new distributed channel allocation algorithm and evaluate its performance through extensive simulation studies. The performance evaluation results obtained under different traffic load and user mobility conditions, show that the proposed algorithm outperforms other algorithms recently proposed in the literature.     

Experimental evaluation of various register‐pressure‐reduction heuristics

Minimizing the amount of spill code is still an open problem in code generation and optimization. The amount of spill code depends on both the register allocation algorithm and the pre‐allocation instruction scheduling algorithm that controls the register pressure. In this paper, we focus on the impact of pre‐allocation instruction scheduling on the amount of spill code. Many heuristic techniques have been proposed to do instruction scheduling with the objective of minimizing register pressure and consequently the amount of spill code. However, the performance of these heuristic techniques has not been studied relative to optimality on real large‐scale programs. In this paper, we present an experimental study that evaluates the performance of several pre‐allocation scheduling heuristics. The evaluation involves computing an experimental lower bound on the size of gap between each heuristic's performance and optimal performance. We also propose a simple heuristic technique based on a specific permutation of two basic priority schemes and experimentally evaluate the performance of this technique compared with other heuristics, including the heuristics implemented in the LLVM open‐source Compiler. The evaluation is carried out by running SPEC CPU2006 on real x86‐64 hardware and measuring both the amount of spill code and the execution time. The results of our study show that the proposed heuristic technique gives better overall performance than LLVM's best heuristic on x86‐64, although it produces slightly more spilling. The proposed heuristic has better overall performance, because it achieves a better balance between register pressure and instruction‐level parallelism (ILP). This result shows the importance of ILP in pre‐allocation scheduling even on out‐of‐order machines. Furthermore, the results of the study show that there is a large gap between the performance of any of the studied heuristics and optimal performance; even the best heuristic in the study produces significantly more spill code than the optimal amount. This experimental result quantifies the intuitive belief that it is unlikely to find a heuristic that works well in all cases, thus showing the need for more rigorous solutions using combinatorial approaches. The paper discusses the challenges and complexities that are involved in developing such rigorous solutions. Copyright © 2014 John Wiley & Sons, Ltd.     

A new hybrid GA solution to combinatorial optimization problems— an application to the multiprocessor scheduling problem

The multiprocessor scheduling problem is one of the classic examples of NP-hard combinatorial optimization problems. Several polynomial time optimization algorithms have been proposed for approximating the multiprocessor scheduling problem. In this paper, we suggest a geneticizedknowledge genetic algorithm (gkGA) as an efficient heuristic approach for solving the multiprocessor scheduling and other combinatorial optimization problems. The basic idea behind the gkGA approach is that knowledge of the heuristics to be used in the GA is also geneticized alongiside the genetic chromosomes. We start by providing four conversion schemes based on heuristics for converting chromosomes into priority lists. Through experimental evaluation, we observe that the performance of our GA based on each of these schemes is instance-dependent. However, if we simultaneously incorporate these schemes into our GA through the gkGA approach, simulation results show that the approach is not problem-dependent, and that the approach outperforms that of the previous GA. We also show the effectiveness of the gkGA approach compared with other conventional schemes through experimental evaluation. This work was presented, in part, at the Second International Symposium on Artifiical Life and Robotics, Oita, Japan, February 18–20, 1997     

Solving constraint optimization problems from CLP-stylespecifications using heuristic search techniques

Presents a framework for efficiently solving logic formulations of combinatorial optimization problems using heuristic search techniques. In order to integrate cost, lower-bound and upper-bound specifications with conventional logic programming languages, we augment a constraint logic programming (CLP) language with embedded constructs for specifying the cost function and with a few higher-order predicates for specifying the lower and upper bound functions. We illustrate how this simple extension vastly enhances the ease with which optimization problems involving combinations of Min and Max can be specified in the extended language CLP* and we show that CSLDNF (Constraint SLD resolution with Negation as Failure) resolution schemes are not efficient for solving optimization problems specified in this language. Therefore, we describe how any problem specified using CLP* can be converted into an implicit AND/OR graph, and present an algorithm called GenSolve which can branch-and-bound using upper and lower bound estimates, thus exploiting the full pruning power of heuristic search techniques. A technical analysis of GenSolve is provided. We also provide experimental results comparing various control strategies for solving CLP* programs     

Optimization of resource allocation for underlay device-to-device communications in cellular networks

Underlay device-to-device (D2D) communication in cellular networks has been considered as a promising technique that can improve the spectral efficiency of cellular systems and meet the growing demand for wireless local services. In underlay D2D, it is of primary importance to manage the mutual interference between cellular links and D2D links through effective resource allocation. While most of previous works on D2D resource allocation are developed based on the knowledge of the channel state information (CSI) on the interference channels as well as the desired channels, it is hard to obtain full CSI in practice. Accordingly, we consider D2D resource allocation schemes based on distance between nodes. In particular, we formulate two optimization problems for D2D resource allocation using the outage probability computed based on the distance information as cost functions. One is a linear sum assignment problem (LSAP) and the other is a linear bottleneck assignment problem (LBAP). By applying the graph theory, we provide efficient algorithms for solving the optimization problems. Numerical results are provided to show the effectiveness of the proposed optimization as compared to previously proposed distance-based resource allocation algorithms.     

Abstract reduction in directed model checking CCS processes

Model checking tools face a combinatorial blow up of the state-space (commonly known as the state explosion problem) that must be addressed to formally verify concurrent systems. We propose an approach combining abstraction techniques and heuristic search to overcome the problem above. In particular, heuristic search can avoid the bottleneck of the exhaustive exploration of the global state graph of a system, while retaining the advantages of abstraction techniques.     

Cellular network configuration with co-channel and adjacent-channel interference constraints

Design of cellular networks has drawn much recent interest from the OR scientific community. A challenging issue is the handling of channel interference constraints. Co-channel interference occurs when the same channel is reused within a threshold distance. Adjacent-channel interference occurs when two channels with adjacent or nearby frequencies are used in the same cell tower. We present a mathematical programming formulation for this channel allocation problem with both types of interference constraints—it also includes decisions on location of cell towers. Our focus is on the special case where a cell tower and/or channel can interfere with at most two other towers/channels. By establishing theoretical properties for channel allocation amongst towers under this circumstance, we develop an efficient solution procedure. An iteration of the procedure uses a heuristic to locate the cell towers, then allocates the channels to the towers using a polynomial-time algorithm, and finally improves this allocation using a simulated annealing procedure. The iterative steps are embedded within an external simulated annealing method. This nested simulated annealing procedure provides encouraging computational results compared to a standard commercial solver like ILOG CPLEX 8.1. The major contribution of the work is the simultaneous consideration of co-channel and adjacent-channel interference constraints.     

Centralized and distributed spectrum channel assignment in cognitive wireless networks: A Harmony Search approach

This paper gravitates on the spectrum channel allocation problem where each compounding node of a cognitive radio network is assigned a frequency channel for transmission over a given outgoing link, based on optimizing an overall network performance metric dependant on the level of interference among nearby nodes. In this context, genetically inspired algorithms have been extensively used so far for solving this optimization problem in a computationally efficient manner. This work extends previous preliminary research carried out by the authors on the application of the heuristic Harmony Search (HS) algorithm to this scenario by presenting further results and derivations on both HS-based centralized and distributed spectrum allocation techniques. Among such advances, a novel adaptive island-like distributed allocation procedure is presented, which dramatically decreases the transmission rate required for exchanging control traffic among nodes at a quantifiable yet negligible performance penalty. Extensive simulation results executed over networks of increasing size verify, on one hand, that our proposed technique achieves near-optimum spectral channel assignments at a low computational complexity. On the other hand, the obtained results assess that HS vastly outperforms genetically inspired allocation algorithms for the set of simulated scenarios. Finally, the proposed adaptive distributed allocation approach is shown to attain a control traffic bandwidth saving of more than 90% with respect to the naive implementation of a HS-based island allocation procedure.     

Design issues of uplink media access control (MAC) protocols for multimedia traffic over DS-CDMA systems

We propose an efficient uplink media access control (MAC) protocol for a variable spreading gain interference-limited wideband CDMA system. It can, with high spectral efficiency, support both real-time traffic like speech and video and also nonreal-time data traffic based on packet transmission. The schemes for power allocation, joint scheduling, and transmission rate adaptation for nonreal time data traffic are designed as integrated parts of the MAC, working together to improve the system performance in terms of capacity and delay. With these associated resource management mechanisms, the performances of the MAC protocol with two different channel-allocation methods for real-time traffic are numerically compared. One is demanding channel allocation, and the other is reserve channel allocation, in which a certain bandwidth is reserved for concurrent real-time traffic.     

An unequal packet loss resilience scheme for video over the Internet

We present an unequal packet loss resilience scheme for robust transmission of video over the Internet. By jointly exploiting the unequal importance existing in different levels of syntax hierarchy in video coding schemes, GOP-level and Resynchronization-packet-level Integrated Protection (GRIP) is designed for joint unequal loss protection (ULP) in these two levels using forward error correction (FEC) across packets. Two algorithms are developed to achieve efficient FEC assignment for the proposed GRIP framework: a model-based FEC assignment algorithm and a heuristic FEC assignment algorithm. The model-based FEC assignment algorithm is to achieve optimal allocation of FEC codes based on a simple but effective performance metric, namely distortion-weighted expected length of error propagation, which is adopted to quantify the temporal propagation effect of packet loss on video quality degradation. The heuristic FEC assignment algorithm aims at providing a much simpler yet effective FEC assignment with little computational complexity. The proposed GRIP together with any of the two developed FEC assignment algorithms demonstrates strong robustness against burst packet losses with adaptation to different channel status.     

Data traffic scheduling algorithm for multiuser OFDM system with adaptive modulation considering fairness among users

Orthogonal frequency division multiplexing (OFDM) is regarded as a very promising digital modulation technique for achieving high rate transmission. However, the increasing number of wireless data users and the deployment of broadband wireless networks have brought about issues of fairness among users and system throughput. In this paper, we propose an efficient scheduling algorithm to maximize system throughput while providing a level of fairness among users for non-real-time data traffic in the downlink of a multiuser OFDM system. We establish a practical scheduling procedure to implement our scheme considering fairness among users and also formulate the resource allocation problem for rate, power, and subcarrier allocation as an integer program that maximizes system throughput. Next, we present a computationally efficient heuristic algorithm for a problem based on the Lagrangian relaxation procedure. Through the computing simulation, we show that the proposed scheme performs better than other schemes in terms of both system throughput and fairness among users.     

UpdateSearch: A New Dynamic Channel Allocation Scheme for Mobile Networks That Can Adjust to System Loads

Distributed dynamic channel allocation techniques are an integral part of distributed mobile computing systems where nodes communicate among themselves via wireless radio channels. The channel allocation schemes can be broadly categorized as search based or update based. Search based techniques have low messaging complexity and are suited for high system load and low request rates. On the other hand update based schemes have higher messaging complexity but are more suitable for low system load and high request rates. This paper presents a combined scheme, called UpdateSearch, which provides the advantages of both types of schemes. UpdateSearch is parameterized by the number of channel classes k, 1kn, where n is the total number of channels in the system. The parameter k can be adjusted to control the number of concurrent searches and degree of contention between cells competing for channels in the system. For k=1 and k=n the scheme respectively behaves as basic search and basic update scheme [2]. A simple analytical model is used to compare the performance of UpdateSearch for various values of k with the basic update and search techniques in terms of channel allocation time and number of simultaneous channel selections allowed in the system under different system loading conditions.     

Subcarrier allocation in multi-hop orthogonal frequency division multiple access wireless networks

Orthogonal frequency division multiplexing (OFDM) has been widely considered as a key technique for next generation mobile communication systems. Meanwhile, relaying technologies can improve users’ quality of service, increase network capacity and enlarge cellular coverage at a low cost. In this paper, we focus on subcarrier allocation and utilization in multi-hop OFDM access (OFDMA) wireless networks, and propose two efficient subcarrier allocation schemes aiming to increase network throughput and subcarrier utilization. The first scheme selects suitable links for data transmission from base stations to terminals at the beginning. Then, interference-free links are included into the same group for network resource reuse. For the purpose of global optimization, we propose a Tabu-based searching algorithm as the second subcarrier allocation scheme. Simulation results demonstrate that our proposed algorithms outperform other schemes in both network throughput and subcarrier utilization.     

On distributed dynamic channel allocation in mobile cellularnetworks

Distributed dynamic channel allocation (DDCA) is a fundamental resource management problem in mobile cellular networks. It has a flavor of distributed mutual exclusion but is not exactly a mutual exclusion problem. We establish the exact relationship between the two problems. Specifically, we introduce the problem of relaxed mutual exclusion to model one important aspect of the DDCA problem. We develop a general algorithm that guarantees relaxed mutual exclusion for a single resource and prove necessary and sufficient conditions for the information structure. Considering distributed dynamic channel allocation as a special case of relaxed mutual exclusion, we apply and extend the algorithm to further address the issues that arise in distributed channel allocation such as deadlock resolution, dealing with multiple channels, design of efficient information structures, and channel selection strategies. Based on these results, we propose an example distributed channel allocation scheme using one of the information structures proposed. Analysis and simulation results are provided and show that the results of this research can be used to design more efficient distributed channel allocation algorithms     

Process Partitioning through Graph Compaction

This paper is concerned with process partitioning that arises in practice in preprocessing of programs for the MIMD parallel machines without shared memory. The graph compaction combinatorial optimization problem is defined and proposed as a model of process partitioning. This problem is proved to be NP-complete. An efficient divide-and-conquer heuristic is given for the solution of the graph compaction problem. By a theoretical analysis based on a random graph model, it is shown that our heuristic produces nearly optimal solutions for an overwhelmingly large proportion of the problem instances of interest. It is shown experimentally that the heuristic compares favorably to heuristics based on commonly used design techniques (neighborhood search, simulated annealing) for random as well as for practical inputs. Since the proposed heuristic has drastically lower running times (seconds vs hours for partitioning about 1000 processes), the experimental results suggest that the algorithm-design approach on which it is based has an advantage over the mentioned approaches when, as in the case of parallel program compilation, good real-time response is desired.     

Utility-based downlink power allocation in multicell wireless packet networks

This paper introduces a utility-based radio resource management technique in multicell wireless packet networks. In terms of allocation of base station (BS) downlink transmit power and assignment of resource to users in each cell, we formulate a problem of maximizing system utility which is defined as the sum of cell utilities. The problem, however, is not solvable due to its non-convex property. Thus, we propose a heuristic algorithm based on an intuition obtained from analyzing a simple two-cell problem. Though the heuristic approach also incurs signaling overhead for power coordination between neighboring base stations, it is much less than that of the original approach. Simulation results show the performance of our proposed algorithm compared with two competitive schemes: optimal and maximum power allocation schemes. As expected, the optimal allocation scheme shows the best performance but can not be employed in a real network due to intractable complexity. Our heuristic algorithm performs reasonably well with very low complexity.     

Stochastic resource allocation using a predictor-based heuristic for optimization via simulation

Some combinatorial stochastic resource allocation problems lack algebraically defined objective functions and hence require optimization via simulation as a mechanism for obtaining good solutions. For this class of problems, we propose a new predictor-based heuristic that uses a distance criterion to perform the solution search. To demonstrate our solution approach, we apply this heuristic to the problem of selecting the proper design configuration of an unmanned aerial system (UAS) fleet so as to maximize mission effectiveness. We compare our approach to black box optimization via simulation approaches (two tabu search-based procedures and a greedy heuristic) and glean both methodological and practical insights.