Multiuser precoding design in multicell-cooperation multiple-input multiple-output (MU-MIMO) systems is a challenging task due to the inevitable information exchange among cooperative base stations (BSs). Based on the productive deep unfolding technique, this paper proposes a multicell-cooperation iterative framework, namely an unfolding synchronous distributed weighted mean-squared-error minimization (USD-WMMSE), to enable distributed and synchronous precoding calculations at the BSs. We develop the USD-WMMSE framework by employing the best response (BR) parallel iteration to decompose the coupling WMMSE precoding in multicell-cooperation MU-MIMO systems into a synchronous distributed scheme. The framework is unfolded into a sequential of neural networks with the iteration step sizes as trainable parameters to accelerate the convergence via unsupervised training. The convergence behavior of the USD-WMMSE is also theoretically proved. Simulation results validate that the proposed USD-WMMSE approaches the sum rate of the centralized WMMSE method with a computational complexity reduction from ${\mathcal { O}}(B<^>{3})$ to ${\mathcal { O}}(B<^>{2})$ with respect to the number of cooperative BSs, B. The proposed USD-WMMSE also exhibits generalizability to various tested scenarios such as different transmit power and iteration numbers.

Millimeter wave (mmWave) systems rely on predefined codebooks for both initial access and data transmission. To compensate the high pathloss of mmWave signal, base station(BS) and user equipment(UE) to be equipped with large antenna arrays which make those codebooks consist of a large number of candidate narrow beams. Both the BS and UE needs to search for a optimal beam from their codebooks that provides maximum received power, such procedure may cause huge beam training overhead. Besides, codebook based beam management limits the maximum beamforming gain as it is bounded by the spatial granularity of the codewords. To overcome these limitations, in the paper, we design a deep learning (DL) based beam training method with partial codebook sweeping. Unlike the existing works using machine learning (ML) or DL to predict the best beam ID from the codebook, the DL model directly outputs the beamforming weights of the analog phase shifters which maximize certain metric, e.g. received signal to noise ratio (SNR). The neural network (NN) is trained offline using simulated environments according to the 3GPP channel models and is then deployed online to predict the optimal beamforming vector with partial beams sensing. Simulation results show that our proposed model outperforms the standard DFT-based codebook with significantly reduced beam training overhead, and enhance the beamforming gain which reflects on the achievable rates.

In this chapter, we investigate the beamforming design for downlink orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems 1 . To enable the stringent URLLC delay requirements, finite blocklength transmission is adopted for the beamforming algorithm design. We formulate the beamforming algorithm design as a non-convex optimization problem for maximization of the weighted system sum throughput subject to constraints on the quality of service (QoS) of the URLLC users. Due to the non-convexity of the optimization problem, finding the optimal solution requires a very high computational complexity which is not affordable for real-time applications. Therefore, to reduce the computational complexity, a sub-optimal algorithm is proposed based on sequential convex pproximation (SCA). Numerical results reveal that the proposed design can achieve a considerable gain compared to several baseline schemes.

We propose a blockage prediction and fast base station (BS) handover (BP-FBSH) scheme based on the reference signal received power (RSRP) of the mobile terminal (MT) and the indices of the BS transmit beams for millimeter wave communications. Using a specific beam tracking method called neighborhood beam search, the BS transmits multiple neighborhood beams to the MT and collects the RSRPs of these beams from the MT. Then, the BP-FBSH scheme uses the beam-associated information sequences composed of the RSRPs and the indices of the BS transmit beams to train a long short-term memory (LSTM)-based blockage prediction neural network (BPNN). If the BPNN predicts the MT is to be blocked, the scheme triggers a handover of this MT to an adjacent BS as well as determining an initial access (IA) beam for it by an LSTM-based BS handover neural network. Simulation results based on Wireless Insite software show that the scheme can achieve high success rate for both the blockage prediction and the IA beam prediction.

Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) are two key emerging technologies for vehicular networks, where NOMA allows multiple vehicular user equipments (VUEs) to share the same wireless resources, and thus to enhance the spectrum utilization and system capacity, and MEC permits VUEs to offload their complex applications to MEC servers, and thus to provide support for computationally intensive intelligent applications. In this article, a NOMA-based vehicle edge computing (VEC) network model is proposed, and the cost minimization problem is constructed. Under the premise of ensuring the delay tolerance of all VUEs, the total system cost is minimized through the joint optimization of offloading decision-making, VUE clustering, subchannel and computation resource allocation, and transmission power control. Since the proposed problem is a mixed-integer nonlinear programming problem, which is difficult to solve, we decouple it into two subproblems and propose two heuristic algorithms to solve the task offloading and the MEC resource assignment problem, respectively, and finally, we obtain the closed-form solutions for cloud-related optimization problems through simple analysis. Simulation results show that the proposed joint algorithm is superior to other baseline algorithms in terms of system cost minimization.

The trend of developing distributed multiple-input multiple-output (MIMO) cooperation has been growing for future wireless networks due to its potential of capacity improvement through network-level precoding. In massive MIMO applications, the overhead of channel state information (CSI) exchange among distributed transmitters is too large to make it possible in practical implementations. In this paper, we consider a cooperative multicell massive MIMO network with distributed regularized zero-forcing (RZF) precoding at each base station (BS), where a novel CSI exchange scheme is devised to reduce the interactive overhead. As a key finding of this work, we theoretically prove that it suffices to share the Gram matrix of local CSI among the cooperative BSs in order to achieve the same performance as a centralized cooperative MIMO network using the RZF precoding with global CSI sharing. The CSI exchange from each BS is thus reduced to a symmetric matrix that has a much smaller size than the full CSI and the amount of CSI exchange does NOT grow with the large number of antennas in massive MIMO. Specifically, based on the exchanged Gram matrices, we derive a decentralized RZF precoding design at each BS and develop both the optimal and suboptimal cooperative power allocation strategies, which achieve different performance and complexity tradeoffs. A virtual centralized power allocation is accomplished at each BS and the performance achieved by the proposed decentralized precoding is the same as the centralized benchmark scheme with full CSI exchange. These superiorities of the proposed schemes are verified through simulation results.

This paper considers optimal joint time, frequency, and power allocation for energy-efficient device-to-device (D2D) communications in an overlay cellular network, where multiple D2D transmitters communicate with their corresponding D2D receivers while utilizing the energy harvested from a cellular base station (BS). Due to channel estimation error and feedback delays, the channel state information (CSI) available at the transmitters is usually imperfect, which can significantly deteriorate the energy efficiency (EE) of the considered system. To mitigate the impact of imperfect CSI, we formulate a non-convex robust optimization problem for the maximization of the EE (in bits/Joule) of the system while ensuring quality of service (QoS) requirements on the minimum data rata and the minimum amount of harvested energy for cellular and D2D users, respectively. We reveal that the optimal energy-efficient D2D transmission should exhaust all the harvested energy of each D2D transmitter. Exploiting this fact, we propose an efficient iterative algorithm for obtaining the optimal solution. To strike a balance between performance and computational complexity, we further propose a low-complexity suboptimal algorithm. Simulation results show that compared with several baseline schemes, both the proposed optimal and suboptimal algorithms can significantly improve the EE of wireless-powered D2D communications under imperfect CSI. Moreover, the proposed suboptimal algorithm is close-to-optimal when the number of cellular users are small or the channel estimation error is large.

In this paper, we investigate robust resource allocation algorithm design for multiuser downlink multiple-input single-output (MISO) unmanned aerial vehicle (UAV) communication systems, where we account for the various uncertainties that are unavoidable in such systems and, if left unattended, may severely degrade system performance. We jointly optimize the two-dimensional (2-D) trajectory and the transmit beamforming vector of the UAV for minimization of the total power consumption. The algorithm design is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the angle of departure (AoD) caused by UAV jittering, user location uncertainty, wind speed uncertainty, and polygonal no-fly zones (NFZs). Despite the non-convexity of the optimization problem, we solve it optimally by employing monotonic optimization theory and semidefinite programming relaxation which yields the optimal 2-D trajectory and beamforming policy. Since the developed optimal resource allocation algorithm entails a high computational complexity, we also propose a suboptimal iterative low-complexity scheme based on successive convex approximation to strike a balance between optimality and computational complexity. Our simulation results reveal not only the significant power savings enabled by the proposed algorithms compared to two baseline schemes, but also confirm their robustness with respect to UAV jittering, wind speed uncertainty, and user location uncertainty. Moreover, our results unveil that the joint presence of wind speed uncertainty and NFZs has a considerable impact on the UAV trajectory. Nevertheless, by counteracting the wind speed uncertainty with the proposed robust design, we can simultaneously minimize the total UAV power consumption and ensure a secure trajectory that does not trespass any NFZ.

In this paper, we study the equalization design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with insufficient cyclic prefix (CP). In particular, the signal detection performance is severely impaired by inter-carrier interference (ICI) and inter-symbol interference (ISI) when the multipath delay spread exceeding the length of CP. To tackle this problem, a deep learning-based equalizer is proposed for approximating the maximum likelihood detection. Inspired by the dependency between the adjacent subcarriers, a computationally efficient joint detection scheme is developed. Employing the proposed equalizer, an iterative receiver is also constructed and the detection performance is evaluated through simulations over measured multipath channels. Our results reveal that the proposed receiver can achieve significant performance improvement compared to two traditional baseline schemes.

In this article, we investigate the resource allocation design for intelligent reflecting surface (IRS)-assisted full-duplex (FD) cognitive radio systems. In particular, a secondary network employs an FD base station (BS) for serving multiple half-duplex downlink (DL) and uplink (UL) users simultaneously. An IRS is deployed to enhance the performance of the secondary network while helping to mitigate the interference caused to the primary users (PUs). The DL transmit beamforming vectors and the UL receive beamforming vectors at the FD BS, the transmit power of the UL users, and the phase shift matrix at the IRS are jointly optimized for maximization of the total spectral efficiency of the secondary system. The design task is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the PUs' channel state information (CSI) and their maximum interference tolerance. Since the maximum interference tolerance constraint is intractable, we apply a safe approximation to transform it into a convex constraint. To efficiently handle the resulting approximated optimization problem, which is still non-convex, we develop an iterative block coordinate descent (BCD)-based algorithm. This algorithm exploits semidefinite relaxation, a penalty method, and successive convex approximation and is guaranteed to converge to a stationary point of the approximated optimization problem. Our simulation results do not only reveal that the proposed scheme yields a substantially higher system spectral efficiency for the secondary system than several baseline schemes, but also confirm its robustness against CSI uncertainty. Besides, our results illustrate the tremendous potential of IRS for managing the various types of interference arising in FD cognitive radio networks.

In this paper, we study resource allocation design for secure communication in intelligent reflecting surface (IRS)-assisted multiuser multiple-input single-output (MISO) communication systems. To enhance physical layer security, artificial noise (AN) is transmitted from the base station (BS) to deliberately impair the channel of an eavesdropper. In particular, we jointly optimize the phase shift matrix at the IRS and the beamforming vectors and AN covariance matrix at the BS for maximization of the system sum secrecy rate. To handle the resulting non-convex optimization problem, we develop an efficient suboptimal algorithm based on alternating optimization, successive convex approximation, semidefinite relaxation, and manifold optimization. Our simulation results reveal that the proposed scheme substantially improves the system sum secrecy rate compared to two baseline schemes.

In this paper, we study resource allocation for downlink orthogonal frequency division multiple access (OFDMA) systems with the objective to enable ultra-reliable low latency communication (URLLC). To meet the stringent delay requirements of URLLC, the impact of short packet communication is taken into account for resource allocation algorithm design. In particular, the resource allocation design is formulated as a weighted system sum throughput maximization problem with quality-of-service (QoS) constraints for the URLLC users. The formulated problem is non-convex, and hence, finding the global optimum solution entails a high computational complexity. Thus, an algorithm based on successive convex optimization is proposed to find a sub-optimal solution with polynomial time complexity. Simulation results confirm that the proposed algorithm facilities URLLC and significantly improves the system sum throughput compared to two benchmark schemes.

In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by the solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the 3D aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an off-line resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixedinteger non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on the online algorithm design that only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the off-line scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on the successive convex approximation. Our simulation results reveal that both the proposed online schemes closely approach the performance of the benchmark off-line scheme and substantially outperform two baseline schemes. Furthermore, our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descends again to a lower altitude to reduce the path loss of the communication links to the users it serves.

We study energy-efficient resource allocation of overlaying device-to-device (D2D) communication in wireless cellular network. In this system, two D2D users first harvest wireless energy from a base station (BS) and then the far D2D user transmits information to the near D2D user. Besides, the near D2D user transmits information of both two users to the BS via NOMA scheme. Due to the interference of NOMA scheme, maximizing energy efficiency (bits/Joule) of the D2D system is a non-convex problem. We formula the problem taking into account the quality-of-service requirements of the D2D users. Exploiting the fractional programming theory and difference of convex (DC) programming, we obtain a sub-optimal resource allocation policy. Our solution reveals that the optimal energy-efficient D2D communication should occur if the far D2D user transmits information to the near D2D user with the minimal required rate. Simulation results are provided to demonstrate the advantage of energy efficiency of the proposed algorithm comparing with two baseline schemes.

In this paper, we study the resource allocation for an orthogonal frequency division multiple access (OFDMA) radio system employing a full-duplex base station for serving multiple half-duplex downlink and uplink users simultaneously. The resource allocation design objective is the maximization of the weighted system throughput while limiting the information leakage to guarantee secure simultaneous downlink and uplink transmission in the presence of potential eavesdroppers. The algorithm design leads to a mixed combinatorial non-convex optimization problem and obtaining the globally optimal solution entails a prohibitively high computational complexity. Therefore, an efficient successive convex approximation based suboptimal iterative algorithm is proposed. Our simulation results confirm that the proposed suboptimal algorithm achieves a significant performance gain compared to two baseline schemes.

Network verification, which is mainly about checking network states against network invariants or operator beliefs, has been a popular research thesis recently. Fast calculation of all-pair reachability of the network beforehand for further query or overall analysis can be a huge assistance to network verification. In this paper, we propose a new fast all-pair reachability calculation algorithm Atomic Predicates Flooding(APF). Experiments on real-life datasets show that the new algorithm based on network atomization is about three to four orders of magnitude faster than existing algorithms without network atomization. On most kinds of datasets, APF is even 2 to 5 times faster than the Warshall based all pair reachability algorithm with atomization. We believe that our method is essential for developing more practical and more efficient network and verification tools.

In this paper, we investigate resource allocation algorithm design for multiuser unmanned aerial vehicle (UAV) communication systems in the presence of UAV jittering and user location uncertainty. In particular, we jointly optimize the two-dimensional position and the downlink beamformer of a fixed-altitude UAV for minimization of the total UAV transmit power. The problem formulation takes into account the quality-of-service requirements of the users, the imperfect knowledge of the antenna array response (AAR) caused by UAV jittering, and the user location uncertainty. Despite the non-convexity of the resulting problem, we solve the problem optimally employing a series of transformations and semidefinite programming relaxation. Our simulation results reveal the dramatic power savings enabled by the proposed robust scheme compared to two baseline schemes. Besides, the robustness of the proposed scheme with respect to imperfect AAR knowledge and user location uncertainty at the UAV is also confirmed.

In this paper, we investigate the resource allocation design for multicarrier (MC) systems employing a solar powered unmanned aerial vehicle (UAV) for providing communication services to multiple downlink users. We study the joint design of the three-dimensional positioning of the UAV and the power and subcarrier allocation for maximization of the system sum throughput. The algorithm design is formulated as a mixed-integer non-convex optimization problem, which requires a prohibitive computational complexity for obtaining the globally optimal solution. Therefore, a low-complexity suboptimal iterative solution based on successive convex approximation is proposed. Simulation results confirm that the proposed suboptimal algorithm achieves a substantially higher system sum throughput compared to several baseline schemes.

In this paper, we investigate resource allocation algorithm design for full-duplex (FD) cognitive radio systems. The secondary network employs a FD base station for serving multiple half-duplex downlink and uplink users simultaneously. We study the resource allocation design for minimizing the max imum interference leakage to primary users while providing qual ity of service for secondary users. The imperfectness of the chann el state information of the primary users is taken into account for robust resource allocation algorithm design. The algorithm design is formulated as a non-convex optimization problem and solv ed optimally by applying semidefinite programming (SDP) relaxation. Simulation results not only show the significant reduction i n interference leakage compared to baseline schemes, but als o confirm the robustness of the proposed algorithm.