Reconfigurable intelligent surface (RIS) is a cutting-edge communication technology that has been proposed as a viable option for beyond fifth-generation wireless communication networks. This paper investigates various group selection strategies in the context of grouping-based self-sustainable RIS-aided device-to-device (D2D) communication with spatially correlated wireless channels. Specifically, we consider both power splitting (PS) and time switching (TS) configurations, of the self-sustainable RIS to analyze the system performance and propose appropriate bounds on the choice of system parameters. The analysis takes into account a simplified linear energy harvesting (EH) model as well as a practical non-linear EH model. Based on the application requirements, we propose various group selection strategies at the RIS. Notably, each strategy schedules thek-th best available group at the RIS based on the end-to-end signal-to-noise ratio (SNR) and also the energy harvested at a particular group of the RIS. Accordingly, by using tools from high order statistics, we derive analytical expressions for the outage probability of each selection strategy. Moreover, by applying the tools from extreme value theory, we also investigate an asymptotic scenario, where the number of groups available for selection at an RIS approaches infinity. The nontrivial insights obtained from this approach is especially beneficial in applications like large intelligent surface-aided wireless communication. Finally, the numerical results demonstrate the importance and benefits of the proposed approaches in terms of metrics such as the data throughput and the outage (both data and energy) performance.

To cope with high bandwidth demands of modern applications, device-to-device (D2D) communications using millimeter-wave (mmWave) signals are being harnessed. The major challenge of mmWave signals is that they require a strict, obstacle-free line-of-sight communication. Static obstacles are easier to avoid; dynamic obstacles pose the main hurdle, their movement not being known. In this work, we propose a way to learn link blockages due to dynamic obstacles, using the link activation history. For this, one might have to explore non-optimal link activations. This ensures that all links are tried a sufficient number of times, ensuring adequate knowledge about link failures, thus creating an exploration-exploitation dilemma. To this end, we propose a systematic way of exploring such non-optimal channel allocations, so that the number of link failures is minimized. Given the hardness of this problem, we devise a greedy solution, and show its effectiveness over existing strategies through simulations.

The millimeter-wave (mmWave) device-to-device (D2D) communication is already being employed to satisfy the high datarate demand of the internet-of-things nodes. Using mmWave signals has its own challenges as it suffers from high penetration losses. Therefore, presence of dynamic obstacles further complicates the already hard problem of allocation of channel resources to the demanding nodes. In this work, we have proposed a reinforcement learning (RL) based framework to jointly allocate the frequency channels as well as assign the transmit-powers to the demanding D2D pairs in order to maximize the energy-efficiency in presence of dynamic obstacles. We justify our choice of reward function through a formal proof and also ensure the convergence of the algorithm. Through extensive simulations, we show that our proposed RL framework not only converges, but also outperforms an existing approach.

Millimeter wave (mmWave) device to device (D2D) communication is highly susceptible to obstacles due to severe penetration losses. Dynamic obstacles may cause unpredictable fluctuations to D2D channel quality and hence a D2D relay initially chosen by base station (BS) might undergo failed transmissions resulting in severe packet loss and delay. This local information regarding link quality deterioration needs to be informed to the BS by the user equipments (UEs) which may result in some delay. Also, exploring a new relay on mmWave channel results in significant delay due to directional search. Hence the following optimal sequential decision must be made when packet loss occurs: whether to explore for a new relay link considering exploration cost, or to continue communication via the existing relay. We model this sequential decision problem locally at each UE as partially observable Markov decision process to capture uncertainty in D2D links while minimizing delay. We derive an optimal threshold policy for both positively and negatively correlated links. We also provide a simplified and easy to implement policy based on successive acknowledgment failures for positively correlated links. Through simulation, we validate theoretical findings and demonstrate that our approach outperforms existing state of the art algorithms.

In this work, a network selection mechanism for re-configurable intelligent surface (RIS) assisted network has been proposed. The goal of the proposed mechanism is to select appropriate base station (BS) or BS-RIS pair to maximize user throughput while satisfying the delay and reliability constraints of uRLLC users. First, the network selection mechanism for RIS assisted network has been formulated as multi arm bandit (MAB) problem; Then, based on the formulation, a network selection strategy has been proposed. Finally, through extensive system level simulations, it has been shown that the proposed scheme outperforms the existing A3 handover scheme in terms of reliability and user throughput.

Blockage due to obstacles in millimeter wave (mmWave) device to device (D2D) communication is a prominent problem due to their severe penetration losses. Potential user equipments (UEs) in vicinity of the source UE must be explored in order to select a new relay when the current link gets blocked. However, dynamic obstacles are not known in advance and thus may cause unpredictable fluctuations to D2D channel quality causing newly selected relay link also to be susceptible to blockage. This might cause frequent relay switching leading to call drops and high energy consumption. We have proposed the idea of reducing frequency in relay exploration and switching and thus average end-to-end delay (in seconds) at the expense of additional exploration time units (few milliseconds) during beam alignment. We seek to learn the uncertainty in D2D link qualities by modeling the problem as finite horizon partially observable Markov decision process (POMDP) framework locally at each UE. We have derived an optimal threshold policy which maps the state to set of actions. We then give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgment successes/failures for making decisions of selecting or not selecting a given relay locally. Through extensive simulation, we validate our theoretical findings and demonstrate that our approach captures the tradeoff between average exploration time and average end-to-end (E2E) delay in presence of dynamic obstacles.

Use of millimeter waves for short range device to device (D2D) communication is one of the most promising ways to provide high data rates. But millimeter waves are susceptible to blockages due to high penetration losses and hence require almost a line of sight (LoS) communication to provide such high data rates. When the direct LoS of a pair of devices is blocked by an obstacle, a reconfigurable intelligent surfaces (RIS) in the vicinity can be exploited to bypass the obstacle and achieve an indirect LoS between them. In this paper, we develop a strategy for placing minimum number of RISs in an environment filled with obstacles such that any pair of devices in the environment who does not have direct LoS can communicate via these RISs by establishing an indirect LoS. We convert this RIS placement problem to a set cover problem and give a greedy algorithm to get a near optimal but practical solution. This is done as a preprocessing step before the actual devices are deployed in the region. Next we present a strategy which knowing the actual deployment of the devices, selects a subset of the deployed RISs for operation together with their channel coefficients and phase shifts such that the system sum throughput of the devices is maximised. Finally simulation results show that strategically placed RISs can significantly improve system throughput in comparison to the existing deployment strategies.

Designing resource block (RB) allocation mechanism for reconfigurable intelligent surface (RIS) assisted wireless networks is quite limited in the preceding literature. In RI S assisted environment, a user equipment (UE) receives throughput through two channels: firstly, through the direct channel from base station (BS) to UE; secondly, through the indirect channel from BS to UE via the RIS. The existing RB allocation mechanism considers only the direct channel conditions. However, in RIS assisted environment, RB selection based on only direct channel condition may not be the optimal one. This is because, a distant UE can achieve a high throughput through the indirect channel even if the direct channel condition is not good. In this work, first, we formulate the RB allocation problem in RIS assisted environment as a multi arm bandit (MAB) problem. Then based on the MAB formulation, we develop a two-phase$\epsilon$-greedy algorithm which explicitly considers the throughput achievable through the indirect channel while selecting RBs. In exploitation phase, it selects the RB providing maximum reward with a probability$1-\epsilon$. In exploration phase, any available RB is selected randomly with a probability$\epsilon$. Finally, through extensive system level simulations, we have shown that our proposed algorithm performs better than an existing proportional fairness based RB allocation mechanism in terms of call dropping probability and system throughput.

In recent past, a new dual connectivity (DC) mechanism has been standardized to deal with two different radio access technologies, namely long term evolution advanced (LTE-A) and new radio (NR). The DC mechanism calls for a higher number of component carrier (CC) allocation which in turn causes increased power consumption. Moreover, to maximize the gain in DC mode, throughput received from the serving base stations need to be nearly equal. The existing CC allocation mechanisms do not take care of these facts. In this work, we first formulate the CC selection problem for LTE-NR DC as an integer linear program (ILP) which accounts load balancing across the serving BSs while minimizing CC allocation. The considered CC selection problem has also been shown to be NP hard. Due to such characteristics, we propose a suboptimal greedy algorithm to solve the CC selection problem. Simulation results confirm that our proposed algorithm outperforms the existing CC selection algorithms.

Millimeter wave (mmWave) device to device (D2D) communication is highly susceptible to obstacles due to severe penetration losses and requires almost a line of sight (LOS) communication path. D2D channel condition is local to devices/user equipments (UEs) and hence is not directly visible to the base station (BS). Thus quality of the D2D channel needs to be propagated to BS by UEs which may incur some delay. Hence the solution provided by BS to UEs using this gathered channel information might become less useful to establish communication due to moving obstacles. These types of obstacles might not be known in advance and hence may cause unpredictable fluctuations to the D2D channel quality. Hence we seek to learn the D2D channels using the finite horizon partially observable Markov decision process (POMDP) framework to model the uncertainty in such kind of network environments with dynamic obstacles. The objective is to minimize delay when channel quality deteriorates, by making UEs choose locally the best possible decision between i) to continue on the current relay link on which communication is taking place or ii) to switch to another good relay by exploring other possible UEs in its locality. We derive an optimal threshold policy which tells the UE to take appropriate decision locally. Later, we give a simplified and easy to implement stationary threshold policy which counts the number of successive acknowledgement failures, based on which UE make appropriate decision locally. Through extensive simulation, we demonstrate that our approach outperforms recent algorithms.

Millimeter wave (mmWave) channels in device to device (D2D) communication are susceptible to blockages in spite of using directional beams from multi-input multi-output (MIMO) antennas to compensate for high propagation loss. This motivates one to look for the presence of obstacles while forming D2D links among user equipments (UEs) which are in motion. In D2D communication, moving UEs also act as relays to forward data from one UE to another which introduces the problem of relay selection. The problem becomes more challenging when the obstacles are also in motion (dynamic obstacles) along with the moving UEs. First we have developed a probabilistic model for relay selection which considers both moving UEs and dynamic obstacles. Then we have analyzed the probability of dynamic obstacles blocking a link in 3D Euclidean space by exploiting the information from MIMO radar connected to the base station. Finally, using this information, we have developed unique strategies based on simple geometry to find the best relay which maximizes the expected data rate. Through simulations we have shown that our proposed strategy gives a significant improvement in packet loss due to mobility of nodes and dynamic obstacles in a mmWave channel over the traditional approaches which do not consider dynamic obstacle's presence.

We formulate the joint power and channel allocation problem (JPCAP) for device to device (D2D) communication as a cost minimization problem, where cost is defined as a linear combination of the number of channels used and total power requirement. We first show that JPCAP is NP-hard and then propose a greedy channel and power allocation (GCPA) algorithm to assign channels and powers to the links. We design GCPA in such a fashion that there exists an order of the links for which it produces optimum solution. Finally using GCPA we develop a randomized algorithm (RA) that increase the optimum hitting probability by an exponential factor of total number of links. Through simulation, we evaluate the performance of RA and show that RA outperforms an existing approach.

In near future, the spectrum resources shared by primary networks to spectrum pools are likely to use unlicensed band because of its increasing popularity. Due to the open access nature, in unlicensed band communication, eavesdroppers are capable of overhearing the traffic channels. This may lead to significant throughput degradation. Existing network selection mechanisms for cognitive radio networks (CRNs) do not consider this security threat adequately. In this work, we first propose a Q-learning based throughput estimation mechanism considering the possibility of throughput degradation caused by eavesdroppers. Then, based on estimated throughput values, we formulate the network selection problem in orthogonal frequency division multiple access (OFDMA) based CRNs as an integer linear program (ILP). Next, based on the ILP formulation, we propose a greedy algorithm for network selections in OFDMA based CRNs. Finally, performance of our proposed network selection mechanism has been compared with existing approaches through system level simulations.

Channel assignment problem in hexagonal cellular networks for multimedia services in a 2-band buffering environment is addressed here. We consider the simplest case of only two types of multimedia services, each having different bandwidths. We first derive lower bounds under different conditions on the minimum bandwidth requirement for assigning multimedia channels to a seven-node subgraph of the hexagonal cellular network by using a novel technique where assignment of different types of signals to support multimedia services are interleaved in the assignment process for the minimum bandwidth. We next propose a multimedia channel assignment algorithm to satisfy the non-homogeneous demands at each node of the network. To evaluate the performance of our proposed algorithm, we present new benchmark instances for multimedia communication. Our proposed algorithm, when run on different benchmark instances, comes up with assignments which require a minimum of 7 percent and a maximum of 40 percent more bandwidth than their corresponding lower bounds. The execution time for all the modified Philadelphia benchmark instances defined on a 21-node network is always within 265 milliseconds on an HPxw8400 workstation, while that for the modified benchmark instances defined on a 55-node network is within 733 milliseconds.

Guaranteeing the quality of service (QoS) for real time applications is one of the challenging issues in next generation all-IP networks. Analysing the user perceived throughput is essential to measure the QoS experienced by real time applications. Medium access control (MAC) mechanism of the serving access network as well as the vertical handover (VHO) algorithm used by the mobile terminals (MTs) for target network selection have significant impact on user perceived throughput. Apart from that the upper layer mobility management protocol also influences the throughput performance considerably. The existing frameworks for throughput analysis do not adequately address the MAC specific issues. In this work, we have developed an analytical framework to analyse user perceived throughput considering the effect of both MAC specific details and upper layer mobility management protocol. Using our proposed framework, we have compared the throughput performance of video traffic application when mobile IPv6 (MIPv6) and seamless IP diversity based generalized mobility architecture (SIGMA) are used for mobility management. Our analysis reveals that the superiority of SIGMA over MIPv6 is actually conditional on the performance of the underlying VHO algorithm. Extensive simulations have been done to validate the analytical results.

Multiple overlapping transmissions cause a serious performance degradation in dense wireless local area network (WLAN) due to increased level of interferences. The issue of interference management becomes extremely challenging particularly for IEEE 802.11 b and g standards where the number of non-overlapping channels (NOCs) is only 3. Power control at the access points (APs) plays a major role to interference management as it determines the transmission radii of the APs. But inappropriate determination of transmission power may put several users out of the coverage range. Efficient use of partially overlapping channels (POC) may lead to increased frequency reuse as it reduces the interference range. But excessive use of POCs may cause reduced quality of service (QoS) due to increased adjacent channel interferences. In this paper we propose an effective power control and POC assignment algorithm that jointly considers power tuning and POC assignment along with NOC to maximize the network performance. Through extensive simulation the performance of the algorithm is evaluated to establish the importance of considering these two factors jointly. Furthermore it has been shown that the proposed approach outperforms some existing approaches.

In cognitive radio systems, fast and efficient spectrum selection is a vital task to minimize the overhead of spectrum scanning, and hence to improve the response time of the system. So, the choice of channel sensing sequence plays an important role for better performance of the system. This paper proposes a co-operative Q-learning based spectrum sensing technique for the secondary users of an ad hoc network to access the primary channels. By the proposed technique, every secondary user (SU) maintains a dynamic priority list of channels based on Q-learning from its own action-observation history, as well as from spatial channel information exchange among its local neighbors. Whenever there is a demand an SU scans the spectrum according to the order in the priority list until there is a success. Simulation studies show that with significantly less computing and scanning overhead, our proposed Q-learning based approach improves the response time and call block/drop rate to offer better performance compared to other contemporary reinforcement learning based approaches.

The fifth generation (5G) ultra dense network (UDN) is envisioned as a very dense deployment of low power base stations where heterogeneous radio access technologies are used to satisfy the data rate demand of users employing both licensed and unlicensed spectrum. In UDN scenario, conditions of the channels operating in licensed band may exhibit intermittent characteristics due to the varying level of interference received from large number of nearby access networks. On the other hand, channel conditions in unlicensed band may fluctuate drastically due to the interference caused by the co-existence of long term evolution (LTE-U) and wireless local area network (WLAN) in unlicensed band. The traditional handover mechanisms rely on the instantaneous assessments of link qualities such as RSS and SINR. The target network selected based on such instantaneous values may not be the appropriate one when the actual handover is executed. In this work, we have proposed a predictive handover mechanism which can estimate achievable throughput values from different candidate access networks after handoff execution. Simulation results confirm that our proposed predictive handover mechanism significantly outperforms an existing reference base station efficiency based approach.

Device to device (D2D) communication in 5G is on the spurge to compensate for the exponential increase in mobile users and their data requirements. In D2D communication, proximity users can communicate and control the links among themselves without the need of base station. This brings a real challenge to model the network to incorporate uncertainty which arises due to the mobility of users. We have developed a stochastic integer programming (SIP) based model to optimize the average network delay while keeping the packet loss below a threshold. This SIP model involves a probabilistic constraint which deals with conditional probability of link breakage in the next time instance given it was active at current time instance. By exploiting the SIP model, we have developed a greedy metric termed as connectivity factor (CF) which captures the nodes' mobility and hence takes care of link reliability which in turn controls packet loss and delay per hop. Based on CF we give a pure distributed greedy algorithm for forwarding the packets to an appropriate next hop node. Advantage of our algorithm to find an appropriate next hop node is that it requires only to compute the expectation and variance of path loss, which in turn can be computed from the distributions of mobility related parameters. Here we have used random walk mobility model for simplicity, but it can be extended to other mobility model with known relevant distributions. Through simulation we have shown that our proposed algorithm gives a significant improvement in packet loss due to mobility of nodes over the traditional received signal strength (RSS) based approach and an existing contention based forwarding (CBF) approach.

With the advent of smarter technologies in cellular networks, often the bands used for lower versions remain unoccupied. To utilize that, in this paper, a new paradigm of cognitive radio has been proposed, where the nodes of a self-organized opportunistic ad hoc network act as the secondary users (SU) to use the white spaces of the existing cellular network. Each SU can freely move around, and in a self-organized fashion may collaborate with other neighboring SUs to gather information on the channels assigned to the cells of the primary network for cognitive use of the licensed spectrum with reduced spectrum latency. Simulation studies show that our proposed cooperative approach significantly improves the call drop/ block rate, and also results better QoS compared to the non-cooperative approach at the cost of negligible additional message overhead.

The channel assignment problem in mobile networks can be modeled as a temporal graph coloring problem where a temporal graph represents a sequence of graphlets generated over a regular interval of time. The cost of coloring a graphlet is defined as a function of number of colors used in the current graphlet and the number of color changes from the previous graphlet. A differential coloring technique is proposed which first finds the minimum number of vertices that requires recoloring and then recolor them. A prediction based and a random coloring based approaches are proposed to reduce the cost. In prediction based approach, we predict a graph which is a supergraph of the graph representing the union of current and next k graphlets and then color it. Whereas, in random coloring we color the graphlets individually. We have shown that both approaches perform better than an existing SNAP algorithm.

In heterogenous network, there are possibilities of horizontal handover between similar type of transmitters as well as vertical handover between different type of transmitters. Also, an user may be served by a single transmitter under the hard handover or may be served by multiple transmitters simultaneously under the soft handover. Considering all these, an user may have several available options to connect with. We have developed a greedy algorithm which allocates every user to their respective most beneficial link in terms of power requirement and traffic cost. We have compared our proposed algorithm with two existing handoff decision algorithms of which one is based on received signal strength indicator measurements and the other is based on selecting the least congested transmitter. Through simulation we have shown that the proposed algorithm outperforms both these algorithms in terms of user coverage and the average data rate provided to the users.

Channel bonding allows IEEE 802.11n clients to operate at a maximum rate of 300 Mbps which can significantly improve the network throughput. But at the same time it increases the co-channel interference to the nearby APs leading to a degradation of network throughput. The issue becomes more serious when the network consists of heterogeneous clients. An inappropriate assignment of bonded channels to the APs may penalize the legacy clients served by other APs. Furthermore, activating the APs with non-bonded channels only may also penalize the n clients. In both cases the network throughput may be compromised. In this paper we propose an efficient bonded channel assignment algorithm addressing how efficiently a bonded channel can be used together with non-bonded channels to maximize the network throughput. Through extensive simulation we have shown that the proposed approach is efficient enough in terms of both network throughput and client fairness.

With increasing popularity of wireless local area network (WLAN) and emerging real time applications, seamless mobility has become one of the primary concerns. Hence the choice of a proper handoff algorithm is of utmost importance. Previous work suggests that WLAN usage efficiency can act as a good metric to measure the performance of a handoff algorithm. We argue that in a multiple AP scenario, the load on each of the APs, the requested data rate of the mobile terminal (MT) and the soft handover have a significant impact on the usage efficiency. In this paper we present an analytical framework to measure the usage efficiency under both load condition and soft handover for a specific data rate request. Our approach is based on finding the circular region centered around an AP within which the requested data rate can be satisfied. The usage efficiency is then computed based on finding the union of all such circular regions.

This paper presents a probabilistic greedy algorithm for solving the channel assignment problem (CAP) in cellular networks. We took each call as a vertex of a complete edge weighed graph, termed as CAP graph, where an edge weight represents the minimum frequency separation needed between the calls represented by the terminal vertices of that edge. Our objective is to assign non-negative integers representing colors or frequencies to the vertices of the CAP graph such that the required span (maximum frequency - minimum frequency) is minimized while satisfying the frequency separation constraints represented by the edge weights. We begin with a probabilistic ordering of the vertices and apply frequency exhaustive strategy to color them. During the coloring, when color of a vertex exceeds the maximum color of previously allocated vertices, we apply a forced assignment phase to reduce the so far obtained span. Finally we propose an iterative compression phase to further reduce the span obtained from applying the frequency exhaustive strategy with forced assignment phase. The proposed polynomial time algorithm is then applied over the well-known benchmark instances and the obtained spans are measured. The obtained results show that the proposed algorithm performs better that the existing assignment strategies with respect to deviation from optimality and computation time. The time taken by our algorithm is less than 1.77 seconds (HP Z400 Workstation) even for the most difficult benchmark instances and thus is very much suitable where fast channel assignment is of primary importance while a marginal deviation from optimality may be tolerated.

Very often a denser deployment of APs is required to increase the coverage and capacity in IEEE 802.11 WLAN. It increases the number of APs with overlapping coverage areas. Such overlapping eventually makes more and more clients to fall in the common coverage area of several APs. Interfering APs require different frequency channels for operation in order to avoid the co-channel interference between the APs. Two clients receiving down-link data from two non-interfering APs operating on the same frequency, may also suffer from interference, if both the clients present within the intersecting coverage area of their associating APs. This type of client interference is very common, as the number of available frequency channels is very limited. Ignorance of both types of interference may significantly overestimates the network performance while independent consideration of the frequency assignment and the association control to deal with such interferences, may lead to a sub-optimal solution. In this paper we argue that both the frequency assignment and the association control play an important role and they should be considered simultaneously to improve the overall network performance. We propose an efficient greedy algorithm that jointly deals with the frequency assignment and association control to maximize the aggregate down-link throughput while avoiding both type of interferences. Through simulation, we have shown that our proposed approach is superior than both the traditional approach of dealing with these interferences and the approach that ignores these interferences altogether.

This paper deals with the problem of optimal association of stations (STAs) to access points (APs) for mulicast services in IEEE 802.11 WLAN. In a multicast session, all the subscribed STAs receive the multicast data packet at the same data rate (Rmin) from their respective serving APs. A higher value of Rminimproves the multicast throughput by completing the ongoing multicast session in lesser time. This also improves the unicast throughput as the cycle duration is shared by the unicast and multicast sessions. To provide multicast services to the STAs, we need to select a minimum cardinality subset of APs as the system message overhead depends on this cardinality. However, such a minimum cardinality subset of APs may not be possible to activate simultaneously due to the limited number of available orthogonal frequency channels. In this paper, we develop a combined greedy algorithm that selects a subset of APs with minimum cardinality for which a conflict-free frequency assignment exists and finds an association between the STAs and the selected APs that maximizes the Rminvalue. Through simulation we have shown that the proposed algorithm selects significantly less number of APs for different Rminvalues in comparison to the well-known metrics for multicast association like RSSI, minimum hop-distance, normalized-cost and in-range STA number.

Cloud computing is fast emerging as a popular choice for a variety of business needs. Providing adequate fault tolerance guarantees to diverse applications is an important challenge. Fault tolerance needs vary from one application to another. Fault tolerance consumes resources. In this paper, we propose fault tolerance to be added as a service, termed here as FTaaS, which can provide both spatial and temporal redundancies. A tenant (who seeks fault tolerance services) can express its intended fault tolerance level as part of the Service Level Agreement (SLA). In this paper, we investigate a general setting, where a tenant can execute in different modes, with different fault tolerance criterion. The task of a provider (who sells fault tolerance services) is to configure his offerings in accordance to the tenants' requirements, in a way which keeps his customers satisfied and his revenue is maximized. We consider several variations of this problem in the paper. We also discuss the other side of the tenant-provider setting, wherein a tenant has multiple providers to choose from, keeping in mind the requirements he has and the cost he has to pay to avail the services. We present formulations of optimization problems related to these in this work.

Frequency channels are allocated to the access-points (APs) satisfying the co-channel interference in an IEEE 802.11 WLAN. Very few APs can be activated simultaneously with a conflict-free frequency assignment to them, due to limited number of frequencies. This may lead to a reduced aggregate throughput. We argue that aggregate throughput can significantly be improved by allowing some of the selected interfering APs to be activated with the same frequency but to operate in a fairly time-scheduled manner to satisfy the co-channel interference. We introduce the notion of class and cluster and based on it propose a combined frequency assignment, AP scheduling and association algorithm with the objective of maximizing the aggregate throughput. Simulation results show that the proposed algorithm is superior than both the algorithms that ignores the co-channel interference altogether by assuming proper frequency planning and that allows to activate only those APs for which a conflict-free frequency assignment exists.

This paper presents a novel method for solving channel assignment problems (CAPs) in hexagonal cellular networks with nonhomogeneous demands in a 2-band buffering system (where channel interference does not extend beyond two cells). The CAP with nonhomogeneous demand is first partitioned into a sequence of smaller subproblems, each of which has a homogeneous demand from a subset of the nodes of the original network. Solution to such a subproblem constitutes an assignment phase, where multiple homogeneous demands are assigned to the nodes corresponding to the subproblem, satisfying all the frequency separation constraints. The whole assignment process for the original network consists of a succession of multiple homogeneous assignments for all the subproblems. Based on this concept, we present a polynomial time approximation algorithm for solving the CAP for cellular networks having nonhomogeneous demands. Our proposed assignment algorithm, when executed on well-known benchmark instances, comes up with an assignment which is always within about 6 percent more than the optimal bandwidth, but requires a very small execution time (less than 5 millisecond on a HPxw8400 workstation). The proposed algorithm is very much suitable for real-life situations, where fast channel assignment is of primary importance, tolerating, however, a marginal deviation (6 percent) from the optimal bandwidth.

We propose a novel scheme for multi-path routing in a Cognitive Radio Network (CRN) for multimedia communication, even when a contiguous band of required width is not available for any hop in the route. The proposed scheme is based on an extension of the idea of Sample Division Multiplexing (SDM) as given in [1] for single-hop communication. Starting from the source node, we first explore the available (free) number of channels (not necessarily contiguous) between every pair of 1-distance neighbor nodes by the help of appropriate control messages. With the information gathered from this step, the network graph G is constructed. We run the max-flow algorithm [8] on this network graph to find the set of routes between the source and the destination nodes so that the sum total of the usable numbers of channels through all these routes is equal to the required number of channels for the multimedia signal to be communicated. In essence, each data packet of the multimedia signal is split into several sub-packets following the basic concept given in [1] each of which needs much smaller bandwidth than the original packet, and these sub-packets are sent through all these routes to be eventually received by the destination node with the desired QoS. The time complexity of our proposed algorithm for this route discovery is O(pf + TSD), where p is the number of edges in the network graph (G), f is the maximum flow in G and TSDis the initialization time for constructing the network graph. We have shown that TSDis O(Δδmax), where Δ is the diameter and δmaxis the maximum node degree of the network graph.

Frequency channels are allocated to the access-points (APs) subject to an acceptable co-channel interference level in an IEEE 802.11 WLAN. Due to limited number of the non-overlapping frequency channels, all APs in a given area may not be activated simultaneously. Therefore which subset of the APs to select for activation is a major concern. Given a set of selected APs, often a station (STA) can potentially associate with more than one AP. The association of an STA to an inappropriate AP will not only lead to degraded service for the concern STA but also may pull down the throughput of the other STAs associated with that AP. Thus finding the optimal association between STAs and APs is another important concern. We argue that treating these two issues in succession may lead to suboptimal solutions, whereas, ignoring the co-channel interference may result over estimation of the throughput. In this paper, an integrated model based on integer programming and an efficient greedy algorithm are proposed that address both aspects simultaneously and maximizes the overall throughput while taking care of load balancing across different APs. Computational results show that indeed the integrated approach is superior to both two-step approach and methods that ignore the interference.

Abstract Efficient allocation of channels for wireless communication in different network scenarios has become an extremely important topic of recent research. The main challenge lies in the fact that the channel allocation problem is NP‐complete. Because of a maximum allowable time limit imposed in practical situations for allocation of channels, sometimes we may need to be satisfied with a near‐optimal solution. In this correspondence, we present a discussion on the various challenges and approaches that have been used by different researchers to solve the problem of channel allocation taking into account different interference issues and efficient utilization of available communication channels for cellular mobile (including multimedia communication) environment and cognitive radio based networks. Copyright © 2010 John Wiley & Sons, Ltd.

Wireless mesh networks (WMN) must often be upgraded as usage demands evolve. This is usually done by adding gateways which serve to increase the backhaul capacity of the network. In this paper we study the problem of this type of capacity augmentation using free-space optical (FSO) backhaul links. A joint clustering and gateway placement problem is formulated which includes the strong rate-distance dependence of practical FSO links. The formulation incorporates the positions of existing wireline gateways and minimizes the number of additional hybrid-FSO/RF gateways which are needed to satisfy the new target capacity requirements. A genetic algorithm solution is proposed, and the performance of our algorithm is compared to an optimal solution generated using an integer linear programming (ILP) formulation. Various scenarios are considered which demonstrate the value of using FSO backhaul links to obtain post-deployment capacity upgrades in response to evolving user traffic.

Hybrid WLAN mesh networks use a combination of nodes that are continuously powered and those that are powered using an energy sustainable source such as solar power. In this paper we consider the problem of cost-optimal placement of the energy sustainable nodes in these types of hybrid networks. We first introduce a cost model that takes into account the provisioning required to operate the solar/wind powered nodes subject to a desired node outage criterion. We then formulate the design problem as a Mixed Integer Quadratic Problem (MIQP). A branch and bound approach is used to obtain node positioning solutions and is compared with a proposed algorithm that uses optimum shortest path routes. Our results show that there is a significant improvement in cost that can be obtained using the proposed methodology and that the branch and bound approach achieves the optimum assignment for a variety of network examples.

Given a set S of n radio-stations on a 2D plane and an integer h, the range assignment problem is to assign ranges to the members in S such that each member of S can communicate with all other members in S using at most h hops, and the sum of powers required for all the members in S is minimized. The general 2D h-hop range assignment problem is known to be NP-hard (A.E.F. Clementi et al, Proc. Symp. on Theor. Aspects of Comp. Sci. (STACS-00), pp. 651-660, 2000). We first consider some simplified variations of the problem and propose an efficient polynomial time algorithm for obtaining optimal solution. In the homogeneous version, where the range assigned to each radio-station is same (/spl rho/), we can obtain the minimum value of /spl rho/ in O(n/sup 3/logn) time in the worst case. In addition, if we consider the unbounded version of the homogeneous range assignment problem (i.e. h=n-1), then the optimal value of /spl rho/ can be obtained in O(n/sup 2/logn) time. Finally, we propose an efficient heuristic algorithm for the general h-hop range assignment problem in 2D, where the range of the radio stations may not be equal. Experimental results demonstrate that our heuristic algorithm runs fast and produces near-optimal solutions on randomly generated instances.