Rate Balancing in Full-Duplex MIMO Two-Way Relay Networks

Maximizing the minimum rate for a full-duplex multiple-input multiple-output (MIMO) wireless network encompassing two sources and a two-way (TW) relay operating in a two hop manner is investigated. To improve the overall performance, using a zero-forcing approach at the relay to suppress the residual self-interference arising from full-duplex (FD) operation, the underlying max-min problem is cast as an optimization problem which is non-convex. To circumvent this issue, semidefinite relaxation technique is employed, leading to upper and lower bound solutions for the optimization problem. Numerical results verify that the upper and lower bound solutions closely follow each other, showing that the proposed approach results in a close-to-optimal solution. In addition, the impact of residual self-interference upon the overall performance of the network in terms of the minimum rate is illustrated by numerical results, and for low residual self-interference scenarios the superiority of the proposed method compared to an analogous half-duplex (HD) counterpart is shown

Asymptotic Close To Optimal Joint Resource Allocation and Power Control in the Uplink of Two-cell Networks

In this paper, we investigate joint resource allocation and power control mechanisms for two-cell networks, where each cell has some sub-channels which should be allocated to some users. The main goal persuaded in the current work is finding the best power and sub-channel assignment strategies so that the associated sum-rate of network is maximized, while a minimum rate constraint is maintained by each user. The underlying optimization problem is a highly non-convex mixed integer and non-linear problem which does not yield a trivial solution. In this regard, to tackle the problem, using an approximate function which is quite tight at moderate to high signal to interference plus noise ratio (SINR) region, the problem is divided into two disjoint sub-channel assignment and power allocation problems. It is shown that having fixed the allocated power of each user, the subchannel assignment can be thought as a well-known assignment problem which can be effectively solved using the so-called Hungarian method. Then, the power allocation is analytically derived. Furthermore, it is shown that the power can be chosen from two extremal points of the maximum available power or the minimum power satisfying the rate constraint. Numerical results demonstrate the superiority of the proposed approach over the random selection strategy as well as the method proposed in [3] which is regarded as the best known method addressed in the literature

Resource Allocation for UAV-Assisted Industrial IoT User with Finite Blocklength

We consider a relay system empowered by an unmanned aerial vehicle (UAV) that facilitates downlink information delivery while adhering to finite blocklength requirements. The setup involves a remote controller transmitting information to both a UAV and an industrial Internet of Things (IIoT) or remote device, employing the non-orthogonal multiple access (NOMA) technique in the first phase. Subsequently, the UAV decodes and forwards this information to the remote device in the second phase. Our primary objective is to minimize the decoding error probability (DEP) at the remote device, which is influenced by the DEP at the UAV. To achieve this goal, we optimize the blocklength, transmission power, and location of the UAV. However, the underlying problem is highly non-convex and generally intractable to be solved directly. To overcome this challenge, we adopt an alternative optimization (AO) approach and decompose the original problem into three sub-problems. This approach leads to a sub-optimal solution, which effectively mitigates the non-convexity issue. In our simulations, we compare the performance of our proposed algorithm with baseline schemes. The results reveal that the proposed framework outperforms the baseline schemes, demonstrating its superiority in achieving lower DEP at the remote device. Furthermore, the simulation results illustrate the rapid convergence of our proposed algorithm, indicating its efficiency and effectiveness in solving the optimization problem.Comment: This paper is accepted by IEEE VTC 2023-Fall, Hong Kong, Chin

Energy and Spectral Efficiency Tradeoff in OFDMA Networks via Antenna Selection Strategy

In this paper, we investigate the joint resource allocation and antenna selection algorithm design for uplink orthogonal frequency division multiple access (OFDMA) communication system. We propose a multi-objective optimization framework to strike a balance between spectral efficiency (SE) and energy efficiency (EE). The resource allocation design is formulated as a multi-objective optimization problem (MOOP), where the conflicting objective functions are linearly combined into a single objective function employing the weighted sum method. In order to develop an efficient solution, the majorization minimization (MM) approach is proposed where a surrogate function serves as a lower bound of the objective function. Then an iterative suboptimal algorithm is proposed to maximize the approximate objective function. Numerical results unveil an interesting tradeoff between the considered conflicting system design objectives and reveal the improved EE and SE facilitated by the proposed transmit antenna selection in OFDMA systems.Comment: This paper is Accepted by IEEE Wireless Communications and Networking Conference (WCNC

Multi-Objective Optimization for Energy-and Spectral-Efficiency Tradeoff in In-band Full-Duplex (IBFD) Communication

The problem of joint power and sub-channel allocation to maximize energy efficiency (EE) and spectral efficiency (SE) simultaneously in in-band full-duplex (IBFD) orthogonal frequency-division multiple access (OFDMA) network is addressed considering users' QoS in both uplink and downlink. The resulting optimization problem is a non-convex mixed-integer non-linear program (MINLP) which is generally difficult to solve. In order to strike a balance between the EE and SE, we restate this problem as a multi-objective optimization problem (MOOP) which aims at maximizing system's throughput and minimizing system's power consumption, simultaneously. To this end, the \epsilon constraint method is adopted to transform the MOOP into single-objective optimization problem (SOOP). The underlying problem is solved via an efficient solution based on the majorization minimization (MM) approach. Furthermore, in order to handle binary subchannel allocation variable constraints, a penalty function is introduced. Simulation results unveil interesting tradeoffs between EE and SE.Comment: This paper is accepted by IEEE Global Communications Conference 201

Energy-Aware Resource Allocation and Trajectory Design for UAV-Enabled ISAC

In this paper, we investigate joint resource allocation and trajectory design for multi-user multi-target unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC). To improve sensing accuracy, the UAV is forced to hover during sensing.~In particular, we jointly optimize the two-dimensional trajectory, velocity, downlink information and sensing beamformers, and sensing indicator to minimize the average power consumption of a fixed-altitude UAV, while considering the quality of service of the communication users and the sensing tasks. To tackle the resulting non-convex mixed integer non-linear program (MINLP), we exploit semidefinite relaxation, the big-M method, and successive convex approximation to develop an alternating optimization-based algorithm.~Our simulation results demonstrate the significant power savings enabled by the proposed scheme compared to two baseline schemes employing heuristic trajectories.Comment: This paper has been accepted for presentation at IEEE GLOBECOM 202

Performance Trade-off Between Uplink and Downlink in Full-Duplex Communications

In this paper, we formulate two multi-objective optimization problems (MOOPs) in orthogonal frequency-division multiple access (OFDMA)-based in-band full-duplex (IBFD) wireless communications.~The aim of this study is to exploit the performance trade-off between uplink and downlink where a wireless radio simultaneously transmits and receives in the same frequency.~We consider maximizing the system throughput as the first MOOP and minimizing the system aggregate power consumption as the second MOOP between uplink and downlink,~while taking into account the impact of self-interference~(SI)~and quality of service provisioning.~We study the throughput and the transmit power trade-off between uplink and downlink via solving these two problems.~Each MOOP is a non-convex mixed integer non-linear programming~(MINLP)~which is generally intractable. In order to circumvent this difficulty, a penalty function is introduced to reformulate the problem into a mathematically tractable form.~Subsequently,~each MOOP is transformed into a single-objective optimization problem~(SOOP)~via the weighted Tchebycheff method which is addressed by majorization-minimization~(MM)~approach. Simulation results demonstrate an interesting trade-off between the considered competing objectives.Comment: This paper is accepted by IEEE International Conference on Communications (ICC