Integrated sensing and communication (ISAC) is a key enabling technology for sixth-generation (6G) mobile communication systems, achieving seamless integration of communication and sensing functions. Cooperative sensing, where the transmitter and receiver are not co-located, serves as a key enabler for ISAC, significantly enhancing the sensing performance while reducing the implementation complexity of the receiver. However, practical deployments of cooperative sensing still face numerous challenges, such as synchronization and interference. This paper presents a set of advanced beam management methods specifically designed for the cooperative sensing system, offering a comprehensive framework to address these challenges. Specifically, we first analyze the strong/weak path effect (SWPE), a critical phenomenon caused by diverse target reflectivities and propagation paths, which severely degrades both synchronization accuracy and target detection. To counteract this, we propose an adaptive path power allocation method compatible with both all-digital and hybrid beamforming architectures. This method intelligently allocates power across different paths to mitigate the SWPE, thereby ensuring reliable synchronization via the direct path while enhancing the detectability of weak targets. As a result, the proposed method improves the target detection probability by over 30%. Furthermore, an adaptive interference suppression method is designed to reduce interference while maintaining sensing/communication quality, which obtains the SINR gain of around 5 dB, compared to the traditional full nulling method. Experimental results validate the effectiveness of robust synchronization and our proposed power allocation. This study lays a solid foundation for beamforming optimization in cooperative sensing systems, facilitating high-accuracy sensing and communication in complex environments.

This paper studies an adaptive limited feedback design in a multi-user downlink broadcast channel (BC) for simultaneous wireless information and power transfer. The objective is to maximize the sum-rate of information decoding (ID) users while maintain an minimum energy harvesting (EH) constraint for each EH user. For limited feedback environments, a total feedback bit constraint is applied at the base station and each user adopts a codebook to quantize the normalized channel state information (CSI). We first present a beamforming design for the case of one EH user and one ID user. In addition, for multiple UEs, we propose a sub-optimal beamforming design which generates a dedicated beam for each user and has low complexity. Also, we derive closed-form expressions of the achievable sum rate of the ID users and present an adaptive mode selection algorithm under different EH constraints and total number of feedback bits. Finally, simulation results verify the effectiveness of our proposed schemes.

In this paper, we aim to maximize the total energy efficiency for a multi-cell MIMO broadcast channel with dirty paper coding, where both the base stations and users employ multiple antennas. The EE metric is defined as the ratio of the total sum- rate to the total power consumption. Because the original problem is non-convex and difficult to tackle directly, we employ the fractional programming and iterative linear approximation methods to transform it into a set of sub-problems. After using Lagrange dual decomposition, each sub- problem essentially becomes a precoding design problem in MIMO broadcast channel (BC) and is still non-convex, which is then dealt with via BC- multiple access channel (MAC) duality property. Specifically, we propose a gradient descent (GD) method to compute the uplink precoder in each MAC problem which has low complexity. Thus, the dual BC problem can be solved and each BS can iteratively update its precoding matrices with small amount of information exchange among the base stations. Numerical results validate the better performance of our proposed algorithm over conventional linear precoding method.

This paper focuses on linear precoding designs in multi-user multiple-input multiple-output block diagonalization (BD) relay systems with limited feedback. A base station employs spatial multiplexing transmission based on quantized channel state information (CSI) obtained from a relay. Then the relay transmits the received signal to users equipped with multiple antennas. In this paper, we propose a BD precoding scheme based on minimum mean square error (MMSE) criterion, which results in a closed-form solution. We show that the proposed scheme is robust to both noise and channel uncertainties caused by quantization errors. Also, we extend the proposed scheme to the case with CSI feedback delays. Simulation results verify the effectiveness of our proposed schemes.

In this paper, we focus on maximizing weighted sum energy efficiency (EE) for a multi-cell multi-user channel. In order to solve this non-convex problem, we first decompose the original problem into a sequence of parallel subproblems which can be optimized separately. For each subproblem, a base station employs dirty paper coding to maximize the EE for users within the cell while regulating interference induced to other cells. Since each subproblem can be transformed to a convex multiple-access channel problem, the proposed method provide a closed-form power allocation. Then, based on the optimal covariance matrix, a locally optimal solution is obtained to maximize the sum EE. Finally, simulation results show that our algorithm based on the non-linear precoding achieves close to 20 percent gain than the conventional linear precoding method.

This paper studies the simultaneous wireless information and power transfer (SWIPT) in a multiuser broadcast (BC) multiple-input multiple-output (MIMO) system, in which a base station sends messages to several information decoding (ID) users, and transmits wireless power to multiple energy harvesting (EH) user at the same time. We aim to maximize the sum-rate of the ID users while maintaining a minimum EH constraint for each EH user. Firstly, an optimal rate-energy (R-E) boundary is characterized by using a BC-multiple access channel (MAC) duality derived from dirty paper coding (DPC). Since the complexity of the DPC is quite high due to continuously encoding and decoding at the transceivers, we then propose a sub- optimal algorithm using a weighted minimum mean square error (WMMSE) approach, which has lower complexity and iteratively converges a local optimal point. Finally, the performance comparisons and convergence properties are illustrated by simulation results.

This paper focuses on linear precoding in a cognitive radio (CR) multi-input multi-output (MIMO) network, where a secondary broadcast channel shares the same spectrum with a licensed primary user (PU). Considering cooperative feedback from PU to the secondary transmitter (ST), a linear precoding scheme is proposed to minimize the expected mean square error (MSE) and null out the interference to the PU. The proposed scheme is an improved approach, which is robust to the channel uncertainties caused by quantization errors and the lack of channel quality information (CQI). Furthermore, interference power control (IPC) is applied at the ST to adjust the transmit power under the tolerable interference threshold of the PU. Simulation results show the effectiveness of the proposed scheme compared with the zero-forcing (ZF) and the conventional minimum mean square error (C-MMSE) criteria.

In this paper we proposed a novel cochannel interference avoidance strategy for outdoor remote medical monitor network. We employ simulated annealing to design the channel allocation algorithm, which is the core of the strategy. We implemented the strategy in the experimental we built at Beijing outer suburb and took a series of experiments to examine the effect of the strategy. The experimental results showed that the interference level in the network was well controlled after the strategy is used.

Distributed multiple-input multiple-output (DMIMO) system is a combination of distributed antenna systems (DAS) and co-located antenna systems (CAS). Power allocation between different antennas is a very important aspect in both DAS and CAS. In this paper, we investigate the downlink power allocation scheme in D-MIMO systems based on minimum symbol error rate (SER) criteria. We propose a power allocation scheme which can effectively approximate the optimal allocation scheme. In the proposed scheme, both Large-scale fading and transceiver antenna correlation are taken into consideration. Moreover, mobile terminal (MT) only needs to feedback largescale fading information and the rank of transmit antenna correlation matrix periodically, which significantly reduces the feedback overhead. Simulation results verify the capacity improvement based on the proposed power allocation scheme.

In many practical systems, perfect channel state information (CSI) is difficult to obtain at the base station (BS), we usually use limited feedback CSI to reduce the symbol error rate (SER). To analyze the SER performance of different transmit distributed antenna (DA) subset in limited feedback distributed antenna systems (DAS), the closed-form SER expressions for both binary phase shift keying (BPSK) and M-phase shift keying (MPSK) are derived in this paper. From these expressions, we find there exists an appropriate transmit DA subset which can obtain the best SER performance. Then, an adaptive transmit DA selection scheme with the minimum SER is proposed. Simulation results show that the proposed scheme can achieve SER performance gain than blanket transmission scheme and selection diversity scheme.