Mathew Goonewardena

Novel retransmission scheme for energy harvesting transmitter and receiver

We consider a point-to-point wireless link with automatic repeat request (ARQ) based packet transmission where both the transmitter and receiver nodes are energy harvesting (EHNs). Transmitter EHN has access to low-grade channel state information (CSI) as it is implicitly obtained from ARQ feedback. Furthermore, signal processing tasks such as sampling and decoding at the receiver EHN can be interrupted if there is insufficient energy in the battery that cause loss of packet and wastage of harvested energy both at the transmitter and receiver EHNs. We propose selective sampling (SS) scheme where only part of the transmitted packet is sampled and stored depending on the receiver nodes stored energy. SS information (SSI) is then fed back to the transmitter. Packet decoding is not performed until full packet is constructed. Hence, we modify the conventional ARQ messages, i.e., ACK/NAK by adding few more bits to carry additional SSI as well. Another objective is to find the optimal power allocation policy to adapt to the low-grade CSI and SSI available at the transmitter such that harvested energy can be utilized efficiently especially at the receiver. Furthermore, using a decision-theoretic framework, we propose greedy power allocation scheme to evaluate the performance of the proposed retransmission scheme. In numerical examples, we illustrate that our proposed scheme has lower average packet transmission time and packet drop probability (PDP) compared to the equal power allocation and greedy power allocation with conventional retransmission scheme.

User–Base-Station Association in HetSNets: Complexity and Efficient Algorithms

This paper considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, namely, maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard, and in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, this paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branch-and-bound CPLEX-based algorithm, and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs.

Energy Management for Energy Harvesting Wireless Sensors With Adaptive Retransmission

This paper analyzes the communication between two energy harvesting wireless sensor nodes. The nodes use automatic repeat request and forward error correction mechanism for the error control. The random nature of available energy and arrivals of harvested energy may induce interruption to the signal sampling and decoding operations. We propose a selective sampling scheme, where the length of the transmitted packet to be sampled depends on the available energy at the receiver. The receiver performs the decoding when complete samples of the packet are available. The selective sampling information bits are piggybacked on the automatic repeat request messages for the transmitter use. This way, the receiver node manages more efficiently its energy use. Besides, we present the partially observable Markov decision process formulation, which minimizes the long-term average pairwise error probability and optimizes the transmit power. Optimal and suboptimal power assignment strategies are introduced for retransmissions, which are adapted to the selective sampling and channel state information. With finite battery size and fixed power assignment policy, an analytical expression for the average packet drop probability is derived. Numerical simulations show the performance gain of the proposed scheme with power assignment strategy over the conventional scheme.

Competition vs. cooperation: A game-theoretic decision analysis for MIMO HetNets

This paper addresses the problem of competition vs. cooperation in the downlink, between base stations (BSs), of a multiple input multiple output (MIMO) interference, heterogeneous wireless network (HetNet). This research presents a scenario where a macrocell base station (MBS) and a cochannel femtocell base station (FBS) each simultaneously serving their own user equipment (UE), has to choose to act as individual systems or to cooperate in coordinated multipoint transmission (CoMP). The paper employes both the theories of non-cooperative and cooperative games in a unified procedure to analyze the decision making process. The BSs of the competing system are assumed to operate at the maximum expected sum rate (MESR) correlated equilibrium (CE), which is compared against the value of CoMP to establish the stability of the coalition. It is proven that there exists a threshold geographical separation, dth, between the macrocell user equipment (MUE) and FBS, under which the region of coordination is non-empty. Theoretical results are verified through simulations.

Generalized Satisfaction Equilibrium for Service-Level Provisioning in Wireless Networks

In this paper, a generalization of the satisfaction equilibrium (SE) for games in satisfaction form (SF) is presented. This new solution concept is referred to as the generalized satisfaction equilibrium (GSE). In games in SF, players choose their actions to satisfy an individual constraint that depends on the actions of all the others. At a GSE, players that are unsatisfied are unable to unilaterally deviate to be satisfied. The concept of GSE generalizes the SE in the sense that it allows mixed-strategy equilibria in which there exist players who are unable to satisfy their individual constraints. The pure-strategy GSE problem is closely related to the constraint satisfaction problem and finding a pure-strategy GSE is proven to be NP-hard. The existence of at least one GSE in mixed strategies is proven for the class of games in which the constraints are defined by a lower limit on the expected utility. A dynamics referred to as the satisfaction response is shown to converge to a GSE in certain classes of games. Finally, Bayesian games in SF and the corresponding Bayesian GSE are introduced. These results provide a theoretical framework for studying service-level provisioning problems in communications networks as shown by several examples.

Fair Scheduling for Energy Harvesting Nodes

This letter considers the problem of scheduling in the multiple input multiple output (MIMO) multiple-access wireless channel, where the transmitters are energy harvesting nodes (EHNs) that are powered by renewable energy sources (RESs). In this letter the conventional scheduling objective of maximizing rate is augmented by two other objectives, regulating fairness, and stabilization of the stored energy processes of the EHNs. This problem is formulated as a network of energy queues, which represent the batteries. Considering the stochastic nature of the wireless channel and the energy harvesting processes, this letter employs Lyapunov drift plus penalty technique to develop a cross-layer scheduler that operates in a slotted-time and distributed manner. At each epoch it selects an EHN for transmission and computes the transmit power. As an added advantage, the power control algorithm still retains the optimal water-filling solution. Through simulations, the proposed solution is compared against a conventional max-rate scheduler and is shown to better enforce fairness, stabilize the battery levels, and minimize the required battery capacity.

Opportunistic distributed channel access for a dense wireless small-cell zone

This paper considers uplink channel access in a zone of closed-access small-cells that is deployed in a macrocell service area. All small-cell user equipments SUEs have access to a common orthogonal set of channels, leading to intercell interference. In addition, each channel forms a separate collision domain in each cell, thus can be successfully used only by one SUE of that cell. This paper proposes two non-cooperative Bayesian games, G1 and G2, that are played among the SUEs. G1 assumes the availability of channel state information at the transmitters, while G2 assumes the availability of only the distribution of the channel state information. Each SUE can choose to transmit over one of the channels or not to transmit. The emphasis of the paper is on the set of symmetric threshold strategies where the Nash equilibrium is fully determined by a single parameter. The existence and uniqueness of pure Bayesian-Nash symmetric equilibrium of G1 in threshold strategies and mixed Bayesian-Nash symmetric equilibrium of G2 in uniformly distributed threshold strategies are proven. Numerical results corroborate the theoretical findings and benchmark against another decentralized scheme. Copyright

Existence of Equilibria in Joint Admission and Power Control for Inelastic Traffic

This letter considers the problem of admission and discrete power control, in the interfering-multiple-access channel, with rate constraints on admitted links. This problem is formulated as a normal form noncooperative game. The utility function models inelastic demand. An example demonstrates that in the fading channel, in some networks, a pure strategy equilibrium does not exist with strictly positive probability. Hence, the probability of existence of an equilibrium is analyzed and bounds are computed. To this end, the problem of finding equilibria is transformed into a constraint satisfaction problem. Next the letter considers the incomplete information setting, with compact convex channel power gains. The resulting Bayesian game is proven to possess at least one pure Bayesian-Nash equilibrium in on-off threshold strategies. Numerical results are presented to corroborate the findings.

On minimum-collisions assignment in heterogeneous self-organizing networks

Minimum-collisions assignment (MCA), in a wireless network, is the distribution of a finite resource set, such that the number of neighbor cells which receive common elements is minimized. In classical operator deployed networks, resources are assigned centrally. Heterogeneous networks contain user deployed cells, therefore centralized assignment is problematic. MCA includes orthogonal frequency bands, time slots, and physical cell identity (PCI) allocation. MCA is NP-complete, therefore a potential-game-theoretic model is proposed as a distributed solution. The players of the game are the cells, actions are the set of PCIs and the cost of a cell is the number of neighbor cells in collision. The price of anarchy and price of stability are derived. Moreover the paper adapts a randomized-distributed-synchronous-update algorithm, for the case, when the number of PCIs is higher than the maximum degree of the neighbor relations graph. It is proven that the algorithm converges to a optimal pure strategy Nash equilibrium in finite time and it is robust to node addition. Simulation results demonstrate that the algorithm is sub-linear in the size of the input graph, thus outperforms best response dynamics.

Efficient user and power allocation in femtocell networks

In this paper we consider the problem of user assignment and power allocation in a small cell environment which is one of the most important problems in present wireless cellular network research. We consider a two-tier cellular network where randomly dispersed overlay femtocell base stations (FBSs) coexist with a macrocell. Our objective is to maximize the total number of users served by the FBSs while satisfying their signal to noise and interference (SINR) requirements. This problem is known to be NP-Hard and hence there is no known optimal solution to solve it in polynomial time. First we formulate the problem of maximization of allocated users under SINR constraints with constant transmit power as an integer programming problem. We provide two heuristic polynomial time algorithms. Then we propose a third algorithm for joint power and user allocation. We evaluate the complexity of the proposed algorithms and furthermore compare the results against the brute force optimal solution and a basic random user assignment through simulations. The results demonstrate the performance and the efficiency of the proposed algorithms. We see in the simulation that the best proposed heuristic for maximizing the number of assigned users is only 3% less than the optimal while reducing the power consumption below that of the optimal user assignment algorithm.