Duy Trong Ngo

Joint Subchannel Assignment and Power Allocation for OFDMA Femtocell Networks

In this paper, we propose a joint subchannel and power allocation algorithm for the downlink of an orthogonal frequency-division multiple access (OFDMA) mixed femtocell/macrocell network deployment. Specifically, the total throughput of all femtocell user equipments (FUEs) is maximized while the network capacity of an existing macrocell is always protected. Towards this end, we employ an iterative approach in which OFDM subchannels and transmit powers of base stations (BS) are alternatively assigned and optimized at every step. For a fixed power allocation, we prove that the optimal policy in each cell is to give each subchannel to the user with the highest signal-to-interference-plus-noise ratio (SINR) on that subchannel. For a given subchannel assignment, we adopt the successive convex approximation (SCA) approach and transform the highly nonconvex power allocation problem into a sequence of convex subproblems. In the arithmetic-geometric mean (AGM) approximation, we apply geometric programming to find optimal solutions after condensing a posynomial into a monomial. On the other hand, logarithmic and \underline{d}ifference-of-two-\underline{c}oncave-functions (D.C.) approximations lead us to solving a series of convex relaxation programs. With the three proposed SCA-based power optimization solutions, we show that the overall joint subchannel and power allocation algorithm converges to some local maximum of the original design problem. While a central processing unit is required to implement the AGM approximation-based solution, each BS locally computes the optimal subchannel and power allocation for its own servicing cell in the logarithmic and D.C. approximation-based solutions. Numerical examples confirm the merits of the proposed algorithm.

Resource Allocation for OFDMA-Based Cognitive Radio Multicast Networks With Primary User Activity Consideration

This paper considers the primary user activity or the subchannel availability in optimally distributing the available resources for an orthogonal frequency-division multiple-access (OFDMA) cognitive radio multicast network. For this purpose, a risk-return model is presented, and a general rate-loss function, which gives a reduction in the attainable throughput whenever primary users reoccupy the temporarily accessible subchannels, is introduced. Taking the maximization of the expected sum rate of secondary multicast groups as the design objective, an efficient joint subcarrier and power-allocation scheme is proposed. Specifically, the design problem is solved via a dual optimization method under constraints on the tolerable interference thresholds at individual primary users frequency bands. It is shown that as the number of subcarriers gets large (which is often the case in practice), the dual-domain solution becomes globally optimum with regard to the primal problem. More attractively, the ¿practically optimal¿ performance of this approach is achieved with a substantially lower complexity, which is only linear in the total number of subcarriers as opposed to exponential complexity typically required by a direct search method. Our proposed design is valid for unicast and multicast transmissions and is applicable for a wide range of rate-loss functions, among which, the linear function is a special case. The superiority of the dual scheme is thoroughly verified by numerical examples.

Distributed Interference Management in Two-Tier CDMA Femtocell Networks

This paper proposes distributed joint power and admission control algorithms for the management of interference in two-tier femtocell networks, where the newly-deployed femtocell users (FUEs) share the same frequency band with the existing macrocell users (MUEs) using code-division multiple access (CDMA). As the owner of the licensed radio spectrum, the MUEs possess strictly higher access priority over the FUEs; thus, their quality-of-service (QoS) performance, expressed in terms of the prescribed minimum signal-to-interference-plus-noise ratio (SINR), must be maintained at all times. For the lower-tier FUEs, we explicitly consider two different design objectives, namely, throughput-power tradeoff optimization and soft QoS provisioning. With an effective dynamic pricing scheme combined with admission control to indirectly manage the cross-tier interference, the proposed schemes lend themselves to distributed algorithms that mainly require local information to offer maximized net utility of individual users. The approach employed in this work is particularly attractive, especially in view of practical implementation under the limited backhaul network capacity available for femtocells. It is shown that the proposed algorithms robustly support all the prioritized MUEs with guaranteed QoS requirements whenever feasible, while allowing the FUEs to optimally exploit the remaining network capacity. The convergence of the developed solutions is rigorously analyzed, and extensive numerical results are presented to illustrate their potential advantages.

Distributed Resource Allocation for Cognitive Radio Networks With Spectrum-Sharing Constraints

This paper presents new design formulations that aim at optimizing the performance of an orthogonal frequency-division multiple-access (OFDMA) ad hoc cognitive radio network through joint subcarrier assignment and power allocation. Aside from an important constraint on the tolerable interference induced to primary networks, to efficiently implement spectrum-sharing control within the unlicensed network, the optimization problems considered here strictly enforce upper and lower bounds on the total amount of temporarily available bandwidth that is granted to individual secondary users. These new requirements are of particular relevance in cognitive radio settings, where the spectral activities of primary users are highly dynamic, leaving little opportunity for secondary access. A dual decomposition framework is then developed for two criteria (throughput maximization and power minimization), which gives rise to the realization of distributed solutions. Because the proposed distributed protocols require very limited cooperation among the participating network elements, they are particularly applicable to ad hoc cognitive networks, where centralized processing and control are certainly inaccessible. In this paper, we recommend that the network collaboration is made possible through the implementation of virtual timers at individual secondary users and through the exchange of pertinent information over a common reserved channel. It is shown that not only is the computational complexity of the devised algorithms affordable but that the performance of these algorithms in practical scenarios attains the actual global optimum as well. The potential of the proposed approaches is thoroughly verified by asymptotic complexity analysis and numerical results.

Efficient Resource Allocation for OFDMA Multicast Systems With Spectrum-Sharing Control

This paper considers the important problem of efficient allocation of available resources (such as radio spectrum and power) in orthogonal frequency-division multiple-access (OFDMA)-based multicast wireless systems. Taking the maximization of system throughput as the design objective, three novel efficient resource-allocation schemes with reduced computational complexity are proposed under constraints on total bandwidth and transmitted power at the base station (BS). Distinct from existing approaches in the literature, our formulation and solution methods also provide an effective and flexible means to share the available radio spectrum among multicast groups by guaranteeing minimum numbers of subcarriers to be assigned to individual groups. The first two proposed schemes are based on the separate optimization of subcarriers and power, where subcarriers are assigned with the assumption of uniform power distribution, followed by water filling of the total available transmitted power over the determined subcarrier allocation. In the third scheme, which is essentially a modified genetic algorithm (GA), each individual of the entire population represents a subcarrier assignment, whose fitness value is the system sum rate computed on the basis of the power water-filling procedure. Numerical results show that with a flexible spectrum-sharing control mechanism, the proposed designs are able to more flexibly and fairly distribute the total available bandwidth among multicast groups and, at the same time, achieve a high system throughput.

Nonsmooth Optimization for Efficient Beamforming in Cognitive Radio Multicast Transmission

It is known that the design of optimal transmit beamforming vectors for cognitive radio multicast transmission can be formulated as indefinite quadratic optimization programs. Given the challenges of such nonconvex problems, the conventional approach in literature is to recast them as convex semidefinite programs (SDPs) together with rank-one constraints. Then, these nonconvex and discontinuous constraints are dropped allowing for the realization of a pool of relaxed candidate solutions, from which various randomization techniques are utilized with the hope to recover the optimal solutions. However, it has been shown that such approach fails to deliver satisfactory outcomes in many practical settings, wherein the determined solutions are found to be unacceptably far from the actual optimality. On the contrary, we in this contribution tackle the aforementioned optimal beamforming problems differently by representing them as SDPs with additional reverse convex (but continuous) constraints. Nonsmooth optimization algorithms are then proposed to locate the optimal solutions of such design problems in an efficient manner. Our thorough numerical examples verify that the proposed algorithms offer almost global optimality whilst requiring relatively low computational load.

Resource Allocation for OFDM-Based Cognitive Radio Multicast Networks

In cognitive radio networks with the coexistence of primary and secondary users, the problem of how to optimally allocate available resources (e.g., bandwidth and power) to multicast groups of secondary users that use orthogonal frequency division multiplexing (OFDM) is important. Taking the maximization of the weighted sum rate of such groups as the design objective, we propose a practically optimal subcarrier and power allocation scheme under constraints on the tolerable interference thresholds at individual primary users frequency bands. Specifically, the optimization problem is solved via the dual method, where subcarriers are assigned in a per-tone basis and power is distributed in a water-filling fashion. As the number of subcarriers becomes large, the dual-domain solution becomes the global optimum of the primal problem with the duality gap vanishing to zero. The proposed design is valid for both unicast and multicast transmissions, and its computational complexity is only linear in the number of subcarriers. The effects of adjacent subcarrier nulling technique, which is to reduce mutual interference between primary and secondary frequency bands, on the proposed scheme are also examined. The superiority of the dual approach is confirmed by numerical results.

Distributed Interference Management in Femtocell Networks

This paper considers a two-tier cellular network wherein femtocell users, who communicate with their home-owner-deployed base stations, share the same frequency band with macrocell users by code-division multiple access (CDMA) technology. Since macrocell users have strictly higher priority in accessing the available radio spectrum, their quality-of-service (QoS) performance, expressed in terms of the minimum required signal-to-interference-plus-noise ratio (SINR), should be maintained at all times. Femtocell users, on the other hand, are allowed to exploit residual network capacity for their own communications. In this work, we develop a joint power- and admission-control algorithm for interference management in such two-tier networks. Specifically, throughput-power tradeoff optimization is achieved for femtocell users while all macrocell users being supported with guaranteed QoS requirements whenever feasible. Importantly, the proposed algorithm makes power and admission control decisions in an autonomous and distributive manner with minimal coordination signaling, a desirable feature in two-tier networks where only limited exchange of signaling information can be afforded on backhaul links. Under certain practical conditions, the developed scheme is shown to converge to a stable solution. An effective technique is also proposed to improve the efficiency of such equilibrium in lightly-loaded networks. The performance of our proposed algorithm is demonstrated by numerical results.

Joint Resource Optimization for Multicell Networks With Wireless Energy Harvesting Relays

This paper first considers a multicell network deployment where the base station (BS) of each cell communicates with its cell-edge user with the assistance of an amplify-and-forward (AF) relay node. Equipped with a power splitter and a wireless energy harvester, the self-sustaining relay scavenges radio-frequency (RF) energy from the received signals to process and forward information. Our aim is to develop a resource allocation scheme that jointly optimizes 1) BS transmit power, 2) received power-splitting factors for energy harvesting and information processing at the relays, and 3) relay transmit power. In the face of strong intercell interference and limited radio resources, we formulate three highly nonconvex problems with the objectives of sum-rate maximization, max-min throughput fairness, and sum-power minimization. To solve such challenging problems, we propose applying the successive convex approximation approach and devising iterative algorithms based on geometric programming and difference-of-convex-function programming. The proposed algorithms transform the nonconvex problems into a sequence of convex problems, each of which is solved very efficiently by the interior-point method. We prove that our algorithms converge to the locally optimal solutions that satisfy the Karush-Kuhn-Tucker (KKT) conditions of the original nonconvex problems. We then extend our results to the case of decode-and-forward (DF) relaying with variable timeslot durations. We show that our resource allocation solutions in this case offer better throughput than that of the AF counterpart with equal timeslot durations, albeit at higher computational complexity. Numerical results confirm that the proposed joint optimization solutions substantially improve network performance, compared with cases where the radio resource parameters are individually optimized.

Efficient resource allocation for OFDMA multicast systems with fairness consideration

The problem of how to optimally allocate available resources (e.g., bandwidth and power) in OFDM-based multicast wireless systems is important. Taking the maximization of system throughput as the design objective, an efficient resource allocation scheme is proposed for such systems under constraints on total system bandwidth and transmit power at the base station. Different from existing methods in the literature, our solution also considers the issue of fair resource utilization among multicast groups by guaranteeing minimum numbers of subchannels to be assigned to individual groups according to their respective channel conditions and group sizes. Once the subcarrier allocation is accomplished, the power allocation is performed in a water-filling fashion. The proposed algorithm distributes the throughput more flexibly and fairly between groups and, at the same time, still achieves a high total sum rate. The potential of our approach is confirmed by numerical results.