In this paper, we consider a hybrid scheme of coordinated multi-point (CoMP) technology in MISO heterogeneous communication networks based on power domain non-orthogonal multiple access (PD-NOMA). We propose a novel method based on matching game with externalities to realize the hybrid scheme where the number of the cooperative nodes are variable. Moreover, we propose a new matching utility function to manage the interference caused by CoMP and NOMA techniques. We also devise robust beamforming to cope with the channel uncertainty. In this regard, we focus on both no CSI and partial CSI cases to increase the achievable data rate. We provide the complexity analysis of both schemes which shows that the complexity of the partial CSI approach is more than that of the no CSI method. Results evaluate the performance of proposed CoMP scheme and the sensibility of our methods, Index Terms, CoMP technology, hybrid scheme, matching game with externalities, PD-NOMA, robust beamforming, probabilistic constraint, no CSI, partial CSI.
Authors in @cite_25 , consider an OFDMA network in the uplink transmissions case. They employ a one-to-many matching game theory algorithm for user association and a one-to-one matching game for channel allocation problems. Moreover, the transmission process is assumed in the non-cooperative mode where the CSI is perfectly known. In this paper, we propose a new algorithm for hybrid cooperative node association via many-to-many matching game and sub-channel allocation in PD-NOMA-based MISO system via many-to-one matching game based on the general framework of @cite_25 . Unlike the methods in @cite_25 , there are some extra-interferences due to the NOMA and CoMP in our considered network. Moreover, we assume externalities in our matching game to insure stability of our proposed method.
@cite_25
1987954156 2144593186 2168150810 2546073943
In this paper, we study resource allocation in a downlink OFDMA system assuming imperfect channel state information (CSI) at the transmitter. To achieve the individual QoS of the users in OFDMA system, adaptive resource allocation is very important, and has therefore been an active area of research. However, in most of the the previous work perfect CSI at the transmitter is assumed which is rarely possible due to channel estimation error and feedback delay. In this paper, we study the effect of channel estimation error on resource allocation in a downlink OFDMA system. We assume that each user terminal estimates its channel by using an MMSE estimator and sends its CSI back to the base station through a feedback channel. We approach the problem by using convex optimization framework, provide an explicit closed form expression for the users' transmit power and then develop an optimal margin adaptive resource allocation algorithm. Our proposed algorithm minimizes the total transmit power of the system subject to constraints on users' average data rate. The algorithm has polynomial complexity and solves the problem with zero optimality gaps. Simulation results show that our algorithm highly improves the system performance in the presence of imperfect channel estimation. In this paper, we study resource allocation and relay selection in a decode-and-forward downlink OFDMA cooperative network assuming imperfect channel state information (CSI) at the base station (BS). We assume that due to feedback delay, the BS has only outdated CSI of BS to users and inter-user links. We propose a centralized optimization framework in which the BS takes decisions on the basis of available outdated CSI given the conditional probability distribution of the current CSI. We approach the problem by using dual optimization framework, decompose it into per-subcarrier optimization problem, and develop an optimal resource allocation and relay selection algorithm. The proposed algorithm minimizes the total transmit power of the system by jointly selecting the best relay node for each source-destination pair, and optimally allocating power and subcarriers to the source and relay nodes under constraints on users' conditional expected data rates. The duality gaps of the solution are virtually zero. Furthermore, we show that the per-subcarrier decomposition of the problem reduces the complexity of the solution from exponential to polynomial. Simulation results show that the system performance may be badly affected by feedback delay. Simulation results also show that users' cooperation significantly reduces the power consumption compared to non-cooperative communication. In this contribution, the performance of an uplink CDMA system is analyzed in the context of frequency selective fading channels. Using game theoretic tools, a useful framework is provided in order to determine the optimal power allocation when users know only their own channel (while perfect channel state information is assumed at the base station). We consider the realistic case of frequency selective channels. This scenario illustrates the case of decentralized schemes and aims at reducing the downlink signaling overhead. Various receivers are considered, namely the Matched filter, the MMSE filter and the optimum filter. The goal of this paper is to derive simple expressions for the non-cooperative Nash equilibrium as the number of mobiles becomes large. To that end we combine two asymptotic methodologies. The first is asymptotic random matrix theory which allows us to obtain explicit expressions for the impact of all other mobiles on any given tagged mobile. The second is the theory of non-atomic games along with the Wardrop equilibrium concept which allows us to compute good approximations of the Nash equilibrium as the number of mobiles grow. This paper tackles the problem of joint users’ uplink transmission power and data rate allocation in multi-service two-tier femtocell networks. Each user—either macrocell (MUE) or femtocell user equipment (FUE)—is associated with a two-variable utility function that represents his perceived satisfaction with respect to his allocated resources (i.e., power and rate). User's utility function is differentiated based both on the tier that the user belongs to and the service he requests. The joint resource allocation problem is directly confronted as a two-variable optimization problem and formulated as a non-cooperative game. The theory of supermodular games is utilized towards treating the two-variable optimization problem and the inherent multidimensional competition that arises among the users. The existence of proposed game's Nash Equilibrium (NE) point is analytically shown, while game's convergence to its NE point is proven. A distributed and iterative algorithm for computing the desired NE is introduced, where the optimal values of each user's uplink transmission power and data rate are simultaneously updated at the same step. The performance of the proposed approach is evaluated via modeling and simulation and its superiority compared to other state of the art approaches is illustrated.