Akram Bin Sediq

Optimal Tradeoff Between Sum-Rate Efficiency and Jain's Fairness Index in Resource Allocation

The focus of this paper is on studying the tradeoff between the sum efficiency and Jains fairness index in general resource allocation problems. Such problems are frequently encountered in wireless communication systems with M users. Among the commonly-used methods to approach these problems is the one based on the α-fair policy. Analyzing this policy, it is shown that it does not necessarily achieve the optimal Efficiency-Jain tradeoff (EJT) except for the case of M=2 users. When the number of users M>2, it is shown that the gap between the efficiency achieved by the α-fair policy and that achieved by the optimal EJT policy for the same Jains index can be unbounded. Finding the optimal EJT corresponds to solving a family of potentially difficult non-convex optimization problems. To alleviate this difficulty, we derive sufficient conditions which are shown to be sharp and naturally satisfied in various radio resource allocation problems. These conditions provide us with a means for identifying cases in which finding the optimal EJT and the rate vectors that achieve it can be reformulated as convex optimization problems. The new formulations are used to devise computationally-efficient resource schedulers that enable the optimal EJT to be achieved for both quasi-static and ergodic time-varying communication scenarios. Analytical findings are confirmed by numerical examples.

Performance Analysis of Selection Combining of Signals With Different Modulation Levels in Cooperative Communications

Cooperative relaying introduces spatial diversity through the creation of a virtual antenna array. The vast majority of research in bit-error-rate (BER) performance analysis of selection-combining (SC) schemes used in digital cooperative relaying assumes the modulation level used by both the source and the relay to be the same. This assumption does not necessarily hold when adaptive modulation is implemented. In conventional SC, the branch with the highest signal-to-noise ratio (SNR) is chosen; we refer to this scheme as SNR-based SC (SNR-SC). However, when different modulation levels are employed, the branch that has the maximum SNR may not necessarily be the most reliable branch due to different error-resistance capabilities of the modulation levels. Consequently, the BER-based SC (BER-SC) is a better SC scheme. In BER-SC, the receiver calculates the BER for each branch (using the SNR and the modulation level) and then decodes the signal from the branch that has the minimum BER. In this paper, we provide BER performance analysis for both BER-SC and SNR-SC and show that BER-SC outperforms SNR-SC, with very comparable complexity. Moreover, we analytically quantify the gain achieved by using BER-SC over SNR-SC through asymptotic approximation. We note that BER-SC and SNR-SC schemes are identical when the received signals belong to the same modulation level.

A genetic algorithm based cell switch-off scheme for energy saving in dense cell deployments

The energy consumption of mobile networks is rapidly growing. Operators have both economic and environmental incentives to increase the energy efficiency of their networks. One way of saving energy is to switch off cells during periods of light traffic. However, cell switch-off is a difficult problem to solve through conventional optimization; existing research makes various assumptions to simplify the problem and offers some heuristics to solve it. The problem can be constructed in different ways depending on the system model that is chosen. We examine the cell switch-off problem with the assumption that each user terminal (UT) has a minimum rate requirement, and show that it can be formulated and solved as a binary integer linear programming (BILP) problem when interference is considered to be constant. This formulation is equivalent to the bin-packing problem, which is NP-hard, if the spectral efficiency of each UT to all cells is fixed to a constant. Allowing the interference to be a function of the UT assignment, which allows for a more realistic construction of the problem, increases the complexity even further and thereby necessitates a heuristic method. For this case, we present a genetic algorithm based cell switch-off scheme which offers good results with linear complexity.

Optimal tradeoff between efficiency and Jain's fairness index in resource allocation

In this paper, we study tradeoff policies between efficiency and the Jains fairness index of the benefits received by M users in general resource allocation scenarios. Analyzing the commonly-used α-fair tradeoff policy, it is shown that, except for the case of M =2 users, this policy does not necessarily achieve the optimal Efficiency-Jain tradeoff. In particular, it is shown that, when the number of users M >;2, the gap between the efficiency achieved by the α-fair and the optimal Efficiency-Jain tradeoff policy can be unbounded, for the same Jains index. Finding the optimal Efficiency-Jain tradeoff for arbitrary set of admissible benefits is generally difficult. To alleviate this difficulty, we derive sufficient conditions, which, when satisfied by the set of admissible benefits, lead to efficiently computable optimal tradeoff and benefit vectors. Numerical results for a typical communication network scenario are provided to confirm analytical findings.

A novel distributed inter-cell interference coordination scheme based on projected subgradient and network flow optimization

In this paper, we propose a novel distributed inter-cell interference coordination (ICIC) scheme. The proposed scheme, which runs in polynomial time, finds a near-optimum dynamic resource partitioning that maximizes a proportional-fairness criterion in the entire network. The proposed scheme is based on primal-decomposition method, where the problem is divided into a master and multiple sub-problems. The master-problem is solved using projected-subgradient method while each of the sub-problems is solved using minimum-cost network flow optimization. Through extensive simulations of four IMT-advanced scenarios, we quantify the gains achieved using the proposed scheme. We demonstrate that the proposed scheme achieves both high cell-edge throughput, that is comparable to frequency reuse 3, and high aggregate throughput, that is at least as good as the aggregate throughput achieved by frequency reuse 1.

Optimized Nonuniform Constellation Rearrangement for Cooperative Relaying

The performance of cooperative relaying networks can be significantly enhanced by using constellation rearrangement (CoRe). In CoRe, the base-station and the relay-station use different constellations, each having the same number of signal points, to communicate with the user terminal. A number of CoRe schemes have been proposed in the literature based on uniform quadrature-amplitude modulation (QAM) constellations. However, it is still unclear whether nonuniform QAM constellations can further enhance the performance of CoRe. Toward this end, we investigate the problem of designing the optimum nonuniform QAM constellations for CoRe. Our motivation is that nonuniform constellations have the potential to outperform their uniform counterparts because the set of nonuniform constellations is a superset of uniform constellations. Nonuniform QAM constellations can be categorized as either decomposable or nondecomposable . Unlike nondecomposable QAM constellations, decomposable QAM constellations are generated from the Cartesian product of two pulse-amplitude modulation (PAM) constellations. We formulate an optimization problem to find the nonuniform constellations that have the minimum union bound on the uncoded symbol error rate (SER). Using convex analysis, we devise a search method to find globally optimum nonuniform decomposable constellations. We also devise a simple heuristic to find good locally optimum nonuniform nondecomposable constellations, which perform better than their decomposable counterparts.

Performance Analysis of SNR-Based Selection Combining and BER-Based Selection Combining of Signals with Different Modulation Levels in Cooperative Communications

Cooperative relaying introduces spatial diversity through the creation of a virtual antenna array. The vast majority of the research in digital cooperative relaying assumes the modu- lation level used by both the source and relay to be the same. This assumption does not necessarily hold when adaptive modulation is implemented. In conventional selection combining, the branch with the highest SNR is chosen; we refer to this scheme as SNR- based selection combining (SNR-SC). In this paper, we introduce BER-based selection combining (BER-SC), as an alternative to SNR-SC, to be used in cooperative communications when a relay may use a modulation level different than that of the source. We provide BER performance analysis for the SNR-SC and BER-SC schemes and show that BER-SC significantly outperforms SNR- SC, without any increase in complexity. Moreover, we analytically quantify the gain achieved by using BER-SC over SNR-SC through asymptotic approximation. We note that BER-SC and SNR-SC schemes are identical when the received signals belong to the same modulation level.

Optimized Distributed Inter-Cell Interference Coordination (ICIC) Scheme Using Projected Subgradient and Network Flow Optimization

In this paper, we tackle the problem of multi-cell resource scheduling, where the objective is to maximize the weighted sum-rate through inter-cell interference coordination (ICIC). The blanking method is used to mitigate the inter-cell interference, where a resource is either used with a predetermined transmit power or not used at all, i.e., blanked. This problem is known to be strongly NP-hard, which means that it is not only hard to solve in polynomial time, but it is also hard to find an approximation algorithm with guaranteed optimality gap. In this work, we identify special scenarios where a polynomial-time algorithm can be constructed to solve this problem with theoretical guarantees. In particular, we define a dominant interference environment, in which for each user the received power from each interferer is significantly greater than the aggregate received power from all other weaker interferers. We show that the originally strongly NP-hard problem can be tightly relaxed to a linear programming problem in a dominant interference environment. Consequently, we propose a polynomial time distributed algorithm that is not only guaranteed to be tight in a dominant interference environment, but which also computes an upper bound on the optimality gap without additional computational complexity. The proposed scheme is based on the primal-decomposition method, where the problem is divided into a master-problem and multiple subproblems. We solve the master-problem iteratively using the projected-subgradient method. We also show that each subproblem has a special network flow structure. By exploiting this network structure, each subproblem is solved using the network-based optimization methods, which significantly reduces the complexity in comparison to the general-purpose convex or linear optimization methods. In comparison with baseline schemes, simulation results of the International Mobile Telecommunications-Advanced (IMT-Advanced) scenarios show that the proposed scheme achieves higher gains in aggregate throughput, cell-edge throughput, and outage probability.

Selection Combining of Signals with Different Modulation Levels in Nakagami-m Fading

Conventionally, the BER analysis of selection combining schemes is performed under the assumption that the signals to be combined belong to the same modulation level. This is not necessarily the case in cooperative relaying, where the source and relay may use different modulation levels. Recently, a closed-form BER expression was derived for selection combing of signals with different modulation levels, under the assumption that the source-relay, source-destination, and relay-destination links are all modeled as Rayleigh fading channels. In this letter, we extend the analysis to the asymmetric Nakagami-m fading channel, which is a more versatile channel model. We derive the closed-form BER expression which is expressed in terms of elementary functions. The derived BER expression generalizes many existing expressions in the literature. Simulation results are also presented to confirm the accuracy of the derived results.

Multihop Wireless Channel Models Suitable for Stochastic Petri Nets and Markov State Analysis

In this paper the system analysis of modern wireless systems is simplified by providing simple yet powerful models for the wireless channel in the environment of higher layer abstract system descriptions with generalized stochastic Petri nets (SPN). This modeling approach is capable of deriving performance metrics in terms of packet delays even under heterogeneous, asymmetric, bursty and underutilized traffic conditions, because they are easy to model with SPN. The missing link in wireless systems are suitable channel models, which can now be used as a plug-in submodel inside a larger composite Petri net model. A number of models are proposed, starting from the finite-state Markov channel model approach. Performance results for a multihop relayed transmission under varied traffic load show the utility of this modeling approach.