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Femto base stations (FBSs) are cost-effective choices for wireless service providers (WSPs) to provide better indoor coverage and capacity in cellular networks. With the introduction of femtocells, spectrum allocation among macro base stations (MBSs) and numerous FBSs becomes challenging. WSPs will allocate resources among hierarchical infrastructures to mitigate mutual interference and maximize WSPs' revenue from service provisioning. In this paper, we propose a channel allocation and service provision framework to help WSPs allocate resources and increase revenue. The spectrum allocation and revenue maximization problem is formulated as a convex problem, and a centralized method is proposed to efficiently solve it. We also propose a distributed algorithm called LD (based on Lagrangian decomposition) to achieve a near-optimal solution with low computational and communication complexity, which can be used when the system is on a large scale. The simulation results show that our scheme can significantly improve the system's revenue. [ABSTRACT FROM PUBLISHER]

Secondary users in a cognitive radio mesh network may select from a set of available channels, provided that they do not disrupt communications among primary users. This ability can improve the overall network performance but introduces the question of how to best use the channels. This paper first considers the problem of selecting the channels to use given a routing path such that the end-to-end throughput along the path is maximized. We show that a dynamic programming-based approach can optimally solve the problem and, if the path satisfies a natural condition, in time, be linear in the length (hop count) of the path. In addition, the algorithm can easily be implemented in a distributed fashion. We also examine the harder joint problem of finding the best routing path and channel selection that maximizes the end-to-end throughput. We prove that obtaining a (\2/3 + \epsilon) approximation to the joint problem is NP-hard. We then present a heuristic algorithm for the joint problem and a second heuristic channel-aware routing-only algorithm. Numerical results are provided to demonstrate the effectiveness of the methods on several experimental scenarios. [ABSTRACT FROM PUBLISHER]

Cognitive radio (CR) has been proposed to improve spectral efficiency while avoiding interference with licensed users. In this paper, we propose a resource allocation (RA) algorithm to perform uplink frequency allocation and power allocation among noncooperative multicells with multiuser per cell in CR systems. The maximization of the total information rate of multiple users in one cell is considered for the Rayleigh channel with path loss subject to the power constraint on each user. Since the optimization formulation for rate maximization of multiple users in each cell is an integer optimization problem, the multiple access channel (MAC) technique is proposed. With the aid of MAC, the original integer optimization problem is transformed into a concave optimization problem, thereby establishing a distributed game model in which the base station of each cell, which tries to maximize the sum rate of its own users, is a player. The proposed game-theoretic algorithm for distributed multiuser power allocation is viewed as an extension of the iterative water-filling algorithm, which is applied to distributed single-user power allocation. From the perspective of effectiveness, the proposed game-theoretic algorithm based on the MAC technique is compared with the traditional algorithm based on frequency-division multiplexing access (FDMA). Final numerical results show that the MAC-based RA algorithm can achieve more information rate and better convergence performance than the FDMA-based RA algorithm. [ABSTRACT FROM PUBLISHER]

Beamforming is used in IEEE 802.15.3c networks to avoid high propagation attenuation and path loss and improve the overall system throughput by exploiting spatial channel reuse. In this paper, we introduce design challenges of scheduling in beamforming-enabled IEEE 802.15.3c networks. These challenges include positioning, axis alignment, and interference relation verification. We then propose a joint design of axis alignment, positioning, and scheduling. The objectives of the proposed joint design are to reduce the consumed channel time, increase the degree of spatial channel reuse, and improve the channel utilization. For positioning, we define and prove a sufficient condition for anchor selection to improve positioning accuracy. The designed channel time allocation period (CTAP)-minimized scheduling algorithm is depicted as a two-layer flow graph, and it consists of the following three phases: 1) layer-1 edge construction; 2) layer-2 edge construction; and 3) scheduling. Through the observation of transmission and reception beams, we define a rule to verify the interference relation of two flows. In addition, given correct topology information, we prove that CTAP-minimized uses the least time to serve all data flows. We evaluate and compare our algorithm with existing approaches through simulations. The observed performance metrics include utilized channel time, system throughput, scheduling efficiency, and spatial channel reuse degree. The results show that CTAP-minimized performs well and achieves its objectives. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a wireless amplify-and-forward relay network with one relay node and multiple source-destination pairs/users and propose a pricing framework that enables the relay to set prices to maximize either its revenue or any desirable system utility. Specifically, depending on the quality of the received signals, the relay sets prices and correspondingly charges the users utilizing its resources for their transmissions. The price is determined in such a way that the relay's revenue or system utility is maximized. Given the specified price, the users competitively employ the relay node to forward their signals. We model each user as a rational player, which aims at maximizing its own net utility through power allocation, and analyze the competition among the users within the framework of noncooperative game theory. It is shown that, in the game played by the users, there always exists a unique pure Nash equilibrium point that can be achieved through distributed iterations. Next, subject to the availability of complete information about the users at the relay, we propose a low-complexity uniform pricing algorithm and an optimal differentiated pricing algorithm, in which the relay either charges the users at a suboptimal uniform price or charges different users at different prices. We also show that, by applying the differentiated pricing algorithm that enforces the users to transmit at certain power levels, any system utility can be maximized. Extensive simulations are conducted to quantify the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

We consider a multicell multicarrier system with frequency reuse distance that is equal to one. Allowing all cells to transmit on the whole bandwidth unveils large potential gains in terms of spectral efficiency, in comparison with conventional cellular systems. Such a scenario, however, is often deemed unfeasible because of the strong multiple access interference (MAI) that negatively affects system performance. This paper presents a layered architecture that integrates a packet scheduler with an adaptive resource allocator that was explicitly designed to take care of the MAI. Each cell performs its resource management in a distributed way with no central controller. Iterative resource allocation assigns radio channels to the users to minimize interference. Packet scheduling guarantees that all users get a fair share of resources, regardless of their position in the cell. This scheduler–allocator architecture integrates both goals and is able to self-adapt to any traffic and user configuration. An adaptive distributed load control strategy can reduce the cell load so that the iterative procedure always converges to a stable allocation, regardless of the interference. Numerical results show that the proposed architecture guarantees both high spectral efficiency and throughput fairness among flows. [ABSTRACT FROM PUBLISHER]

In this paper, low-complexity joint power assignment algorithms are developed for multi-source multi-destination relay networks where multiple sources share a common relay that forwards all received signals simultaneously to destinations. In particular, we consider the following power optimization strategies: (i) Minimization of the total transmission power of the sources and the relay under the constraint that the signal-to-interference-plus-noise ratio (SINR) requirement of each source-destination pair is satisfied, and (ii) Maximization of the minimum SINR among all source-destination pairs subject to any given total power budget. Both optimization problems involve K power variables, where K is the number of source-destination pairs in the network, and an exhaustive search is prohibitive for large K. In this work, we develop a methodology that allows us to obtain an asymptotically tight approximation of the SINR and reformulate the original optimization problems to single-variable optimization problems, which can be easily solved by numerical search of the single variable. Then, the corresponding optimal transmission power at each source and relay can be calculated directly. The proposed optimization schemes are scalable and lead to power assignment algorithms that exhibit the same optimization complexity for any number (K) of source-destination pairs in the network. Moreover, we apply the methodology that we developed to solve a related max-min SINR based optimization problem in which we determine power assignment for the sources and the relay to maximize the minimum SINR among all source-destination pairs subject to any given total power budget. Extensive numerical studies illustrate and validate our theoretical developments. [ABSTRACT FROM AUTHOR]

This paper studies optimal bandwidth and power allocation in a cognitive radio network where multiple secondary users (SUs) share the licensed spectrum of a primary user (PU) under fading channels using the frequency division multiple access scheme. The sum ergodic capacity of all the SUs is taken as the performance metric of the network. Besides the peak/average transmit power constraints at the SUs and the peak/average interference power constraint imposed by the PU, total bandwidth constraint of the licensed spectrum is also taken into account. Optimal bandwidth allocation is derived in closed-form for any given power allocation. The structures of optimal power allocations are also derived under all possible combinations of the aforementioned power constraints. These structures indicate the possible numbers of users that transmit at nonzero power but below their corresponding peak powers, and show that other users do not transmit or transmit at their corresponding peak powers. Based on these structures, efficient algorithms are developed for finding the optimal power allocations. [ABSTRACT FROM AUTHOR]

The need to improve the efficiency of elevator systems is growing as higher buildings are being constructed. Increasing the number of elevators within a building is a commonly used strategy to achieve a greater passenger transportation capacity. However, the area required by elevator shafts limits the number of elevators that can be built. Multicar-elevator (MCE) systems, consisting of two or more elevator cars within a single shaft, have been proposed as a solution to increase the passenger transportation capacity without requiring extra space within the building. Yet, the implementation of MCEs requires the prevention of interference among elevator cars, which traditional group control methods are unable to achieve. Therefore, an MCE group control method that can perform an efficient allocation of service calls while preventing interference among cars is required. In this paper, we propose an MCE group control algorithm implementing an interference risk evaluation, a schedule-completion-time minimization function, and idle-car parking strategies. With our algorithm, we aim to achieve an efficient allocation of calls and prevent interference, but without restricting the operation of elevator cars like the existing MCE group control methods do. The simulation results show that our algorithm has a good performance under low and medium passenger arrival rates. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-timescale delay-optimal dynamic clustering and power allocation design for downlink network MIMO systems. The dynamic clustering control is adaptive to the global queue state information (GQSI) only and computed at the base station controller (BSC) over a longer time scale. On the other hand, the power allocations of all the BSs in each cluster are adaptive to both intracluster channel state information (CCSI) and intracluster queue state information (CQSI), and computed at each cluster manager (CM) over a shorter time scale. We show that the two-timescale delay-optimal control can be formulated as an infinite-horizon average cost constrained partially observed Markov decision process (CPOMDP). By exploiting the special problem structure, we derive an equivalent Bellman equation in terms of pattern selection Q-factor to solve the CPOMDP. To address the distributed requirement and computational complexity, we approximate the pattern selection Q-factor by the sum of per-cluster potential functions and propose a novel distributed online learning algorithm to estimate them distributedly. We show that the proposed distributed online learning algorithm converges almost surely. By exploiting the birth-death structure of the queue dynamics, we further decompose the per-cluster potential function into the sum of per-cluster per-user potential functions and formulate the instantaneous power allocation as a per-stage QSI-aware interference game played among all the CMs. The proposed QSI-aware simultaneous iterative water-filling algorithm (QSIWFA) is shown to achieve the Nash equilibrium (NE). [ABSTRACT FROM AUTHOR]

We analyze the sum rate performance in multicell single-hop networks where access points are allowed to cooperate in terms of a joint resource allocation. The resource allocation policies considered here combine power control and user scheduling. Although promising from a conceptual point of view, the optimization of the sum of per-link rates hinges on tough issues such as computational complexity and the requirement for heavy receiver-to-transmitter and cell-to-cell channel information feedback. In this paper, however, we show that simple distributed algorithms can scale optimally in terms of rates, when the number of users per cell U is allowed to grow large. We use extreme value theory to provide scaling laws for upper and lower bounds for the network sum-rate (sum of single user rates over all cells), corresponding to zero-interference and worst-case interference scenarios. We show that the scaling is either dominated by path loss statistics or by small-scale fading, depending on the regime and user location scenario. A surprising result is that the well known \log \log U rate behavior exhibited in i.i.d. fading channels with maximum rate schedulers is transformed into a \log U behavior when path loss is accounted for. Additionally, by showing that upper and lower rate bounds behave in fact identically, asymptotically, our results suggest, remarkably, that the impact of multicell interference on the rate (in terms of scaling) actually vanishes asymptotically, when appropriate resource allocation policies are used. [ABSTRACT FROM AUTHOR]

A constantly available control channel facilitates control message exchange and spectrum coordination in cognitive radio (CR) ad hoc networks. When a dedicated control channel is unavailable, a control channel must be dynamically allocated in licensed channels and vacated for the presence of primary users (PUs). As a result, the establishment of such a control channel is a challenge. In this paper, an efficient recovery control channel (ERCC) design is proposed to address this challenge. This heuristic and distributed design approach is essentially based on the observed spectrum homogeneity in a neighborhood. By adaptively updating a list of channels commonly available to neighbors, each secondary user is able to efficiently establish new control channels among neighbors in response to PU activity changes. Therefore, a virtually “always on” control channel robust to PU activity can be realized by the proposed method. The contributions are summarized as follows: 1) The proposed method efficiently recovers control channels from PU activity changes and maintains network connectivity. 2) It extends the control channel coverage to facilitate broadcast and reduce control overhead and delay. 3) It minimizes the interference with PUs. Simulation results show that the proposed solution outperforms the classic group- and sequence-based solutions in the responsiveness to rapidly changing PU activity and the maintenance of connectivity. Furthermore, the increase in control channel coverage and the allocation of the highest quality channels to control channels can be well balanced with reliability and scalability in various network scenarios. [ABSTRACT FROM PUBLISHER]