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In this paper, we consider an intelligent reflecting surface (IRS)-assisted cognitive radio system and maximize the secondary user (SU) rate by jointly optimizing the transmit power of secondary transmitter (ST) and the IRS’s reflect beamforming, subject to the constraints of the minimum required signal-to-interference-plus-noise ratio at the primary receiver, the ST’s maximum transmit power, and the unit modulus of the IRS reflect beamforming vector. This joint optimization problem can be solved suboptimally by the non-convex optimization techniques, which however usually require complicated mathematical transformations and are computationally intensive. To address this challenge, we propose an algorithm based on the deep deterministic policy gradient (DDPG) method. To achieve a higher learning efficiency and a lower reward variance, we propose another algorithm based on the soft actor-critic (SAC) method. In these proposed algorithms, a reward impact adjustment approach is proposed to improve their learning efficiency and stability. Simulation results show that the two proposed algorithms can achieve comparable SU rate performance with much shorter running time, as compared to the existing non-convex optimization-based benchmark algorithm, and that the proposed SAC-based algorithm learns faster and achieves a higher average reward with lower variance, as compared to the proposed DDPG-based algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the finite-order filter design problem for the source and multiple full-duplex (FD) relays in a cooperative communication system under frequency-selective fading channels. The goal is to optimize the source and relay filters such that the end-to-end signal-to-interference-plus-noise ratio (SINR) of minimum mean-squared error decision-feedback equalizer (MMSE-DFE) can be maximized. The resultant design problem is very difficult since we need to deal with self interference (SI), inter-relay interference (IRI), and inter-symbol interference (ISI) at the same time. Novel designs are then proposed to overcome the difficulty in this work. Transforming the signals into the frequency domain and using some optimization techniques, we first theoretically derive the power spectrum of the source filter and the spectrums of the relay filters. Then, the finite-order design is developed to approach the derived spectrums. Based on the weighted least-square (WLS) criterion, the Steiglitz-McBride method is exploited to obtain the filter coefficients in finite lengths. Numerical results demonstrated that complete removal of SI may not be a good strategy; preserving a certain amount of SI at each relay instead provides better SINR performance. [ABSTRACT FROM AUTHOR]

The emergence of interference alignment (IA) as a degrees-of-freedom optimal strategy motivates the need to investigate whether IA can be leveraged to aid conventional network utility maximization algorithms, which are typically only capable of finding locally optimal solutions. To test the usefulness of IA in this context, this paper proposes a two-stage optimization framework for the downlink of a $G$ -cell multi-antenna network with $K$ users/cell. The first stage of the proposed framework focuses on nulling interference from a set of dominant interferers using IA, while the second stage optimizes transmit and receive beamformers to maximize a network-wide utility using the IA solution as the initial condition. Further, this paper establishes a set of new feasibility results for partial IA that can be used to guide the number of dominant interferers to be nulled in the first stage. Through simulations on specific topologies of a cluster of base stations, it is observed that the impact of IA depends on the choice of the utility function and the presence of out-of-cluster interference. In the absence of out-of-cluster interference, the proposed framework outperforms straightforward optimization when maximizing the minimum rate, while providing marginal gains when maximizing sum rate. However, the benefit of IA is greatly diminished in the presence of significant out-of-cluster interference. [ABSTRACT FROM AUTHOR]

Coordinated multipoint transmission (CoMP) and soft frequency reuse (SFR) techniques are two common mechanisms that are often combined with the aim of reducing the deleterious effects caused by inter-cell interference in current and envisaged cellular networks (4G/5G). The performance of this combined mechanism has been shown to be strongly influenced by the specific choice of the parameters determining the frequency reuse settings. So far, their optimization has relied on time-consuming simulations that make the CoMP-SFR optimization a challenging issue. In an attempt to revert this situation, this paper introduces a novel analytical framework that, for a specific set of SFR parameters, allows the computation of closed-form approximations to relevant performance metrics such as average total capacity, worst-users rate, and backhaul requirements. Remarkably, these approximations exhibit a dependence on the SFR parameters that match that of the true metrics, hence allowing the optimization of the SFR-related parameters based on the proposed analytical expressions. Furthermore, and very important from a practical perspective, the availability of these closed-form expressions allows the efficient application of multi-objective optimization techniques to realistic scenarios with irregularly deployed base stations that serve to evaluate the various tradeoffs the designer is facing when optimizing multiple, and often conflictive, performance metrics. [ABSTRACT FROM AUTHOR]

We consider optimal linear precoder and decoder designs in a multi-cell multiple-input multiple-output system, where base stations and mobile users are both operating in full-duplex (FD) mode. Existing works on FD cellular systems focus on the maximization of overall throughput, which can result in unfairness between uplink and downlink channels depending on the self-interference power and inter-user interference levels. Therefore, to introduce fairness, in this paper, we consider the transmit and receive beamforming designs that maximize the harmonic-sum of signal-to-interference-plus-noise ratios (SINRs) in the uplink and downlink channels. We propose a low-complexity alternating optimization algorithm which converges to a stationary point. Moreover, in order to address practical system design aspects, we consider the transceiver design that enforces robustness against imperfect channel state information (CSI) while providing fair performance among the users. To this end, we formulate an optimization problem that maximizes the worst case SINR among all users under norm-bounded CSI errors. We devise a low-complexity iterative algorithm based on alternating optimization and semidefinite relaxation techniques. Numerical results verify the advantages of incorporating FD mode into cellular systems, and practical issues, such as CSI uncertainty and fairness performance. [ABSTRACT FROM PUBLISHER]

Traditional cellular networks are moving toward heterogeneous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all user equipment (UE), where the CoMP improves the single-path availability, and the CA enhances availability via multi-carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP and CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a two-step optimization algorithm (TSOA) and a joint (JTOA). The TSOA is based on heuristic algorithm for the optimal subcarrier assignment and UE association, and based on the Lagrangian dual method for the power allocation. The JTOA is based on genetic algorithm to achieve the interaction between the first step and the second step. Our results showcase the effective of our proposed JTOA, and the effective of the CoMP in availability improvement in HetNets. [ABSTRACT FROM PUBLISHER]

In this paper, we consider multiple-input single-output (MISO) physical layer multicasting, where several multiantenna transmitters each simultaneously multicast a common message to a distinct set of single-antenna receivers, causing interference between different multicast messages. To optimize the achievable rate of this MISO multicast interference channel, we propose iterative distributed transmit optimization algorithms that are based on interference leakage control, requiring local channel state information at each transmitter and leakage information exchange among transmitters. We, furthermore, propose extensions of existing coordinated multipoint transmission schemes that have been developed for unicast interference channels, such as signal to leakage and noise ratio beamforming, to the considered multicast system. Such methods are of importance, e.g., for future releases of LTE that will support multiantenna multicasting using MBMS/MBSFN. Numerical simulations confirm the potential of the proposed distributed transmit optimization algorithm. [ABSTRACT FROM AUTHOR]

Full-duplex transmission is a promising technique to enhance the capacity of relay communications. In this paper, we investigate the power and location optimization for full-duplex decode-and-forward (DF) relaying systems. The outage probability is adopted as the optimization criterion and a two-dimensional (2D) optimization problem is formulated for systems with and without the direct source–destination link. Analytical and numerical results are provided to demonstrate advantages of the power and location optimization. More interestingly, the effect of residual self-interference (RSI) introduced by full-duplex transmission is investigated in terms of the optimal power allocation and relay location deployment and the outage probability performance improvement. Furthermore, we show that the minimal outage probability can be achieved via joint power-location optimization. The effect of power optimization on the outage performance is also compared between full-duplex and half-duplex relaying. [ABSTRACT FROM AUTHOR]

In this paper, we tackle the problem of multi-cell resource scheduling, where the objective is to maximize the weighted sum-rate through inter-cell interference coordination (ICIC). The blanking method is used to mitigate the inter-cell interference, where a resource is either used with a predetermined transmit power or not used at all, i.e., blanked. This problem is known to be strongly NP-hard, which means that it is not only hard to solve in polynomial time, but it is also hard to find an approximation algorithm with guaranteed optimality gap. In this work, we identify special scenarios where a polynomial-time algorithm can be constructed to solve this problem with theoretical guarantees. In particular, we define a dominant interference environment, in which for each user the received power from each interferer is significantly greater than the aggregate received power from all other weaker interferers. We show that the originally strongly NP-hard problem can be tightly relaxed to a linear programming problem in a dominant interference environment. Consequently, we propose a polynomial time distributed algorithm that is not only guaranteed to be tight in a dominant interference environment, but which also computes an upper bound on the optimality gap without additional computational complexity. The proposed scheme is based on the primal-decomposition method, where the problem is divided into a master-problem and multiple subproblems. We solve the master-problem iteratively using the projected-subgradient method. We also show that each subproblem has a special network flow structure. By exploiting this network structure, each subproblem is solved using the network-based optimization methods, which significantly reduces the complexity in comparison to the general-purpose convex or linear optimization methods. In comparison with baseline schemes, simulation results of the International Mobile Telecommunications-Advanced (IMT-Advanced) scenarios show that the proposed scheme achieves higher gains in aggregate throughput, cell-edge throughput, and outage probability. [ABSTRACT FROM PUBLISHER]