Power allocation based on power efficiency in uplink OFDMA systems: A game theoretic approach

The explosive demands for mobile broadband service bring a major challenge to 5G wireless networks. Device-to-device communication, adopting side links for user-direct communication, is regarded as a main technical source for offloading large volume of mobile traffic from cellular base station. This article investigates the joint power and subcarrier allocation scheme for device-to-device communication in 5G time division duplex systems. In time division duplex system, instead of utilizing an exclusive portion of the precious cellular spectrum, device-to-device pairs reuse the subcarriers occupied by cellular users, thus producing harmful interference to cellular users in both uplink and downlink communication, and strongly limiting the spectrum efficiency of the system. To this end, we focus on the maximization of device-to-device throughput while guaranteeing both uplink and downlink channel quality of service of cellular users as well as device-to-device pairs. The problem is formulated as a mixed integer non-linear programming (MINLP) problem. To make it tractable, we separate the original MINLP problem into two sub problems: power allocation and sub-carrier reusing. The former is to develop optimal power allocation for each device-to-device pair and each cellular user, with the constraints of maximum power and quality of service. It is solved by geometric programming technique in convex optimization method. The latter is derived as a one-to-many matching problem for scheduling multiple subcarriers occupied by cellulars to device-to-device pairs. It is solved by Hungarian method. Simulation results show that the proposed scheme significantly improves system capacity of the device-to-device underlay network, with quality of service of both device-to-device users and cellular users guaranteed.

This paper studies the case of an overlay cognitive radio network where the primary user leases spectral resources to the secondary user in exchange for cooperation, considering that both type of users have specific quality of service requirements. We investigate the problem of joint power and time allocation for the secondary access during the cooperative phase, with a view to optimizing the effective capacity of the primary user given an average energy constraint for the secondary user. Afterwards, the optimal power allocation of the secondary user for its own transmission phase is investigated in order to maximize the effective capacity of the secondary link. The proposed joint power and time allocation mechanism is compared with an optimal time/constant power allocation scheme and a less sophisticated baseline allocation scheme, i.e. power allocation under constant time and its superiority is proven for various network parameters. The reference model of one primary---one secondary user is extended to a general multi user cognitive radio network through the proposed pairing mechanism based on matching theory. Particularly, considering the remarks of the reference scenario, we propose two different matching schemes (with/without consideration of primary users' quality of service requirements) and we confirm their superiority compared to other matching mechanisms.

In this paper, a joint subcarrier and power allocation algorithm is proposed to improve the physical layer security in cooperative orthogonal frequency division multiple access (OFDMA) networks, where several source-destination pairs and one untrusted relay are involved. The relay is friendly and intends to help these pairs to enhance their communications. However, it may be overheard by a malicious eavesdropper at the same time. To optimize the joint subcarrier and power allocation with low complexity, we divide the optimization problem into two simpler subproblems. Firstly, the subcarriers are assigned to the source-destination pairs by employing the dual approach under the assumption that the relay power is equally allocated to all the subcarriers. Then, the relay power is allocated to the subcarriers based on the alternative ascending clock auction mechanism. In addition, we prove that the two subproblems can converge after a finite number of iterations. We also find that the proposed auction is cheat-proof and, thus, can avoid the cheating behaviors in the auction process. Numerical results demonstrate that our proposed algorithm can effectively improve the system sum secrecy rate, and the convergence performance is also desirable.

A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.

Novel closed form formulae of iterative optimal power control and allocation, and a criterion for optimal subcarrier allocation, are derived for uplink of multicarrier non-orthogonal multiple access (MC-NOMA) systems. Our new formulae provide intuitive insights on how to optimally allocate subcarriers and power to MC-NOMA (and as a special case, MC-OMA) users. In particular, the novel water-filling power allocation formulae provide a mean to iteratively compute optimal power allocation for a general NOMA cluster size. Convergence of the iterative algorithm and sum rate performance of MC-NOMA system are presented and compared with MC-OMA system.

In this paper, we explore a joint subcarrier assignment and power allocation with statistical delay QoS guarantees for full-duplex OFDMA systems. Effective capacity theory which evaluates wireless channel capacity from a novel view of link layer provides a theory basis for modeling the system capacity under the conditions of delay QoS constrains. After modeling the aggregate effective capacity of the system, we formulate joint subcarrier assignment and power allocation problem as a mixed integer programming problem, whose target is to maximize the aggregate effective capacity. To solve this integer programming problem, we allocate initial power on each subcarrier and decompose it into two subproblems, which are subcarrier allocation problem and power allocation problem respectively. Further, we propose an alternative algorithm to achieve joint subcarrier assignment and power allocation, which can meet the “subcarrier continuity” constraint of uplink resource allocation. Simulations have shown that the performance of our proposed scheme is favorite. Additionally, we investigate the effect of the residual selfinterference, inter-node interference (INI) and statistical delay QoS exponent on the performance of systems.

A novel cross-layer joint relay selection and power allocation (CL-JRSPA) scheme over physical layer and data-link layer is proposed for cooperative relaying system in this paper. Our goal is finding the optimal relay selection and power allocation scheme to maximize system achievable rate when satisfying total transmit power constraint in physical layer and statistical delay quality-of-service (QoS) demand in data-link layer. Using the concept of effective capacity (EC), our goal can be formulated into an optimal joint relay selection and power allocation (JRSPA) problem to maximize the EC when satisfying total transmit power limitation. We first solving optimal power allocation (PA) problem with Lagrange multiplier approach, and then solving optimal relay selection (RS) problem. Simulation results demonstrate that CL-JRSPA scheme gets larger EC than other schemes when satisfying delay QoS demand. In addition, the proposed CL-JRSPA scheme achieves the maximal EC when relay located approximately halfway between source and destination, and EC becomes smaller when the QoS exponent becomes larger.

Multi-user multiple input multiple output (MU-MIMO) system is an excellent choice for the next generation broadband communications because of its great potential in enhancing MIMO system capacity. One major challenge of such system is the jointly optimal transceiver beamforming design that maximizes the sum capacity under a total power constraint. Existing approaches have provided convex optimization solution by exploiting the duality between transmitter and receiver. However, with high computation complexity, these approaches are ineligible of practical implementation. In this paper, we aim to provide complexity efficient transceiver beamformer and power allocation design in MU-MIMO downlink (broadcast) channels. We first develop an iterative sub-optimal algorithm based on cyclic self-SINR-maximization (CSSM) beamforming design and water-filling power allocation. Comparing to convex-optimization-based (COB) approaches, the proposed solution (i.e. CSSM-WF algorithm) have insignificant sum-rate degradation but very low computational complexity and extremely fast converging speed. Trying to improve the performance of CSSM-WF algorithm, we then introduce another algorithm denoted as efficient COB (ECOB) algorithm in which CSSM-WF algorithm is used to generate a good start point for optimal COB approaches. Simulation results prove the efficiency of both proposed algorithms.

In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.

Embedding Femtocells in a Macro cellular network is a breakthrough to the traditional network, which can improve the spectral utility greatly. However, in two-layer Femtocell-Macrocell networks, exclusive channel assignment can avoid Co-Channel Interference (CCI), but the Frequency utilization rate is very low; common channel assignment can improve the frequency utilization rate but exacerbating the inter-layer interference. This paper presents a frequency assignment based on multiuser subcarrier and power allocation scheme in two-layer in two-layer Femtocell-Macrocell networks, which divide the cell into inner region and outer region by setting coverage area (FCA) threshold of a co-channel Femtocell base station (FBS), to reduce the interference between the layers through allocating different frequency to inter region and outer region. To further enhance performance of the mobile station served by MBS (MMS) in outer region, allocating subcarrier and power for MMSs within the cell, MMSs of the outer region have preference to gain better subcarriers, combined with WSA algorithm to maximize throughput; in power allocation, a hybrid power allocation is adopted for MMSs. Simulation results show that the proposed algorithm reduces the inter-layer interference and improves system throughput.

In this chapter, we investigate one suboptimal power allocation scheme for an OFDM based cognitive radio system. Optical power allocation for secondary user in OFDM subcarrier is complex since it has a power as well as an interference constraint. Mathematical analysis shows that interference between primary and secondary in OFDM based system depends on theirs spectral distance. In our suboptimal method we consider the spectral distance between the secondary and primary user and modify the traditional water-filling algorithm. By applying our suboptimal power allocation method, it is possible that we can get good performance comparable to the optimal scheme. We compare our method with traditional power allocations schemes like equal power allocation and water filling power allocation. Result shows that distance dependent modified water filling (DDMWF) scheme can achieve the highest data rate for the cognitive radio based Secondary user.

For the next generation wireless communication system, Multi carrier Code Division Multiple Access (MC-CDMA) has become very appealing scheme due to due to high data rates and interference limiting factors. In this research work we analyzes the capacity of MC-CDMA system using adaptive power and sub-channel allocation under perfect, imperfect and predicted channel state information (CSI). The transmitter and receiver both have been targeted in this paper. The proposed scheme is a novel idea that divides the sub-channels into small groups. Each group is assigned power according to the Eigen values and gain of the each sub-channel in that specific group. The receiver calculates optimal feedback vector for the transmitter by calculating the bit error rates using the above strategy and selects the optimal feedback vector. The simulations show that the usage of joint power and sub-channel allocation outperforms the capacity of the system. The capacity of the system is highly improved using adaptive power and sub-carriers allocation along with CSI prediction. The simulation results have been carried out for other power allocation schemes available in literature and results have been compared with perfect, imperfect and predicted CSI.

We consider a parallel relay communication network based on the nonorthorgonal amplify-and-forward protocol. We design two optimal power allocation schemes: separate power allocation (SPA) of the relays using the improved water-filling algorithm and joint power allocation (JPA) of the source and relays using convex optimization algorithm. Simulation results show that, in the fading channels with path loss, the proposed JPA and SPA have better mutual information performances than equal power allocation and direct transmission without relay.

Joint resource and trajectory optimization in a UAV-enabled dual-function radar-communication network

Multiuser orthogonal frequency division multiplexing (OFDM) is a very promising multiple access technique to efficiently utilize limited RF bandwidth and transmit power in wideband transmission over multipath fading channels. When a wideband spectrum is shared by multiple users in multiuser OFDM-based systems, different users may experience different fading conditions at all subcarriers. Each user is assigned a subset of all subcarriers by some allocation algorithm. Thus, multiuser diversity can be achieved by adaptively adjusting subcarrier, bit, and power allocation depending on channel status among users at different locations (Wong et al., 1999a). In (Wong et al., 1999a), Wong applies a Lagrangian optimization technique and an iterative algorithm to solve the subcarrier, bit and power allocation problem. The suboptimal scheme for the NP-hard joint optimization problem is decoupled into two steps while it has a high computational complexity. A sub-optimal algorithm has been proposed to solve a related problem (Wong et al., 1999b). In (Wong et al., 1999b), Wong presents a real-time subcarrier allocation (SA) algorithm. It is a two-phase algorithm, including the constructive initial assignment (CIA) and the subcarrier swapping steps. The initial subcarrier allocation algorithm needs to pre-determine the numbers of subcarriers for each user before the allocation process starts. The performance of the SAbased algorithm will be compared in the simulation. In (Kim et al., 2006), Kim shows that the allocation problem in (Wong et al., 1999a) can be transformed into an integer programming (IP) problem. The branch-and-bound algorithm (Wolsey, 1998) can be employed to find the optimal solution of the allocation problem which has exponential computational complexity in the worse cases. We utilize the approach to obtain the optimal solution as the performance bound for comparison. Evolutionary algorithms (EA) are used to solve extremely complex search and optimization problems which are difficult to solve through simple methods. EAs are intended to provide a better solution, as it is based on the natural theory of evolution. Evolutionary algorithm (EA)-based schemes have been applied to solve subcarrier, bit, and power allocation problems (Wang et al., 2005) (Reddy et al., 2007) (Reddy & Phora, 2007) (Pao & Chen, 2008). In general, chromosomes can be designed with binary, integer, or real representation. The chromosome lengths are related to the number of subcarriers. Each element in the chromosome is a subcarrier allocated to a user. In this research, a subset of subcarriers can be assigned to one user depending upon the availability of subcarriers at a particular time