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Cell-free massive multiple-input multiple-output (MIMO) and intelligent reflecting surface (IRS) are considered as the prospective multiple antenna technologies for beyond the fifth-generation (5G) networks. Cell-free MIMO systems powered by IRSs, combining both technologies, can further improve the performance of cell-free MIMO systems at low cost and energy consumption. Prior works focused on instantaneous performance metrics and relied on alternating optimization algorithms, which impose huge computational complexity and signaling overhead. To address these challenges, we propose a novel two-step algorithm that provides the long-term passive beamformers at the IRSs using statistical channel state information (S-CSI) and short-term active precoders and long-term power allocation at the access points (APs) to maximize the minimum achievable rate. Simulation results verify that the proposed scheme outperforms benchmark schemes and brings a significant performance gain to the cell-free MIMO systems powered by IRSs.

With the increasing number of wireless communication systems and the demand for bandwidth, the wireless medium has become a congested and contested environment. Operating under such an environment brings several challenges, especially for military communication systems, which need to guarantee reliable communication while avoiding interfering with other friendly or neutral systems and denying the enemy systems of service. In this work, we investigate a novel application of Rate-Splitting Multiple Access(RSMA) for joint communications and jamming with a Multi-Carrier(MC) waveform in a multiantenna Cognitive Radio(CR) system. RSMA is a robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on multi-antenna Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers. Our aim is to simultaneously communicate with Secondary Users(SUs) and jam Adversarial Users(AUs) to disrupt their communications while limiting the interference to Primary Users(PUs) in a setting where all users perform broadband communications by MC waveforms in their respective networks. We consider the practical setting of imperfect CSI at transmitter(CSIT) for the SUs and PUs, and statistical CSIT for AUs. We formulate a problem to obtain optimal precoders which maximize the mutual information under interference and jamming power constraints. We propose an Alternating Optimization-Alternating Direction Method of Multipliers(AOADMM) based algorithm for solving the resulting non-convex problem. We perform an analysis based on Karush-Kuhn-Tucker conditions to determine the optimal jamming and interference power thresholds that guarantee the feasibility of problem and propose a practical algorithm to calculate the interference power threshold. By simulations, we show that RSMA achieves a higher sum-rate than Space Division Multiple Access(SDMA).

This paper considers a cell-free massive MIMO (CF-mMIMO) system using conjugate beamforming (CB) with fractional-exponent normalization. Assuming independent Rayleigh fading channels, a generalized closed-form expression for the achievable downlink spectral efficiency is derived, which subsumes, as special cases, the spectral efficiency expressions previously reported for plain CB and its variants, i.e. normalized CB and enhanced CB. Downlink power control is also tackled, and a reduced-complexity power allocation strategy is proposed, wherein only one coefficient for access point (AP) is optimized based on the long-term fading realizations. Numerical results unveil the performance of CF-mMIMO with CB and fractional-exponent normalization, and show that the proposed power optimization rule incurs a moderate performance loss with respect to the traditional max-min power control rule, but with lower complexity and much smaller overall power consumption.

Intelligent reflecting surface (IRS) can be densely deployed in wireless networks to significantly enhance the communication channels. In this letter, we consider the downlink transmission from a multi-antenna base station (BS) to a single-antenna user, by exploiting the cooperative passive beamforming (CPB) and line-of-sight (LoS) path diversity gains of multi-IRS signal reflection. Unlike existing works where only one single multi-IRS reflection path from the BS to user is selected, we propose a new and more general multi-path beam routing scheme. Specifically, the BS sends the user's information signal via multiple orthogonal active beams (termed as active beam splitting), which point towards different IRSs. Then, these beamed signals are subsequently reflected by selected IRSs via their CPB in different paths, and finally coherently combined at the user's receiver (thus named {\it \textbf{passive beam combining}}). For this scheme, we formulate a new multi-path beam routing design problem to jointly optimize the number of IRS reflection paths, the selected IRSs for each of the reflection paths, the active/passive beamforming at the BS/each selected IRS, as well as the BS's power allocation over different active beams, so as to maximize the received signal power at the user. To solve this challenging problem, we first derive the optimal BS/IRS beamforming and BS power allocation for a given set of reflection paths. The clique-based approach in graph theory is then applied to solve the remaining multi-path selection problem efficiently. Simulation results show that our proposed multi-path beam routing scheme significantly outperforms its conventional single-path beam routing special case.

Reconfigurable intelligent surfaces (RISs) have emerged as a prospective technology for next-generation wireless networks due to their potential in coverage and capacity enhancement. The analysis and optimization of ergodic capacity for RIS-assisted communication systems have been investigated extensively. However, the Rayleigh or Rician channel model is usually utilized in the existing work, which is not suitable for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. Thus, we fill the gap and consider the ergodic capacity of RIS-assisted mmWave MIMO communication systems under the Saleh-Valenzuela channel model. Firstly, we derive tight approximations of ergodic capacity and a tight upper bound in high signal-to-noise ratio regime. Then, we aim to maximize the ergodic capacity by jointly designing the transmit covariance matrix at the base station and the reflection coefficients at the RIS. Specifically, the transmit covariance matrix is optimized by the water-filling algorithm and the reflection coefficients are optimized using the Riemanian conjugate gradient algorithm. Simulation results validate the tightness of the derived ergodic capacity approximations and the effectiveness of the proposed algorithms.

This paper considers a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) downlink communication system where hybrid analog-digital beamforming is employed at the base station (BS). We formulate a power minimization problem by jointly optimizing hybrid beamforming at the BS and the response matrix at the RIS, under the signal-to-interference-plus-noise ratio (SINR) constraints at all users. The problem is highly challenging to solve due to the non-convex SINR constraints as well as the unit-modulus phase shift constraints for both the RIS reflection coefficients and the analog beamformer. A two-layer penalty-based algorithm is proposed to decouple variables in SINR constraints, and manifold optimization is adopted to handle the non-convex unit-modulus constraints. {We also propose a low-complexity sequential optimization method, which optimizes the RIS reflection coefficients, the analog beamformer, and the digital beamformer sequentially without iteration.} Furthermore, the relationship between the power minimization problem and the max-min fairness (MMF) problem is discussed. Simulation results show that the proposed penalty-based algorithm outperforms the state-of-the-art semidefinite relaxation (SDR)-based algorithm. Results also demonstrate that the RIS plays an important role in the power reduction.

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has recently received significant research attention due to its outstanding ability of serving grant-free (GF) users with grant-based (GB) users' spectrum, which greatly improves the spectrum efficiency and effectively relieves the massive access problem of 5G and beyond networks. In this paper, we first study the outage performance of the greedy best user scheduling SGF scheme (BU-SGF) by considering the impacts of Rayleigh fading, path loss, and random user locations. In order to tackle the admission fairness problem of the BU-SGF scheme, we propose a fair SGF scheme by applying cumulative distribution function (CDF)-based scheduling (CS-SGF), in which the GF user with the best channel relative to its own statistics will be admitted. Moreover, by employing the theories of order statistics and stochastic geometry, the outage performances of both BU-SGF and CS-SGF schemes are analyzed. Theoretical results show that both schemes can achieve full diversity orders only when the served users' data rate is capped, which severely limits the rate performance of SGF schemes. To further address this issue, we propose a distributed power control strategy to relax such data rate constraint, and derive analytical expressions of the two schemes' outage performances under this strategy. Finally, simulation results validate the fairness performance of the proposed CS-SGF scheme, the effectiveness of the power control strategy, and the accuracy of the theoretical analyses.

This paper considers a two-user non-orthogonal multiple access (NOMA) based infrastructure-to-vehicle (I2V) network, where one user requires reliable safety-critical data transmission and the other pursues high-capacity services. Leveraging only slow fading of channel state information, we aim to maximize the expected sum throughput of the capacity hungry user subject to a constraint on the payload delivery success probability of the reliability sensitive user, by jointly optimizing the transmit powers, target rates, and decoding order. We introduce a dual variable and formulate the optimization as an unconstrained single-objective sequential decision problem. Then, we design a dynamic programming based algorithm to derive the optimal policy that maximizes the Lagrangian. Afterwards, a bisection search based method is proposed to find the optimal dual variable. The proposed strategy is shown by numerical results to be superior to the baseline approaches from the perspectives of expected return, performance region, and objective value.

We study the generalized load-balancing (GLB) problem, where we are given $n$ jobs, each of which needs to be assigned to one of $m$ unrelated machines with processing times $\{p_{ij}\}$. Under a job assignment $\sigma$, the load of each machine $i$ is $\psi_i(\mathbf{p}_{i}[\sigma])$ where $\psi_i:\mathbb{R}^n\rightarrow\mathbb{R}_{\geq0}$ is a symmetric monotone norm and $\mathbf{p}_{i}[\sigma]$ is the $n$-dimensional vector $\{p_{ij}\cdot \mathbf{1}[\sigma(j)=i]\}_{j\in [n]}$. Our goal is to minimize the generalized makespan $\phi(\mathsf{load}(\sigma))$, where $\phi:\mathbb{R}^m\rightarrow\mathbb{R}_{\geq0}$ is another symmetric monotone norm and $\mathsf{load}(\sigma)$ is the $m$-dimensional machine load vector. This problem significantly generalizes many classic optimization problems, e.g., makespan minimization, set cover, minimum-norm load-balancing, etc. We obtain a polynomial time randomized algorithm that achieves an approximation factor of $O(\log n)$, matching the lower bound of set cover up to constant factor. We achieve this by rounding a novel configuration LP relaxation with exponential number of variables. To approximately solve the configuration LP, we design an approximate separation oracle for its dual program. In particular, the separation oracle can be reduced to the norm minimization with a linear constraint (NormLin) problem and we devise a polynomial time approximation scheme (PTAS) for it, which may be of independent interest.

We consider the extra degree of freedom offered by the rotation of the reconfigurable intelligent surface (RIS) plane and investigate its potential in improving the performance of RIS-assisted wireless communication systems. By considering radiation pattern modeling at all involved nodes, we first derive the composite channel gain and present a closed-form upper bound for the system ergodic capacity over cascade Rician fading channels. Then, we reconstruct the composite channel gain by taking the rotations at the RIS plane, transmit antenna, and receive antenna into account, and extract the optimal rotation angles after investigating their impacts on the capacity. Moreover, we present a location-dependent expression of the ergodic capacity and investigate the RIS deployment strategy, i.e. the joint rotation adjustment and location selection. Finally, simulation results verify the accuracy of the theoretical analyses and deployment strategy. Although the RIS location has a big impact on the performance, our results showcase that the RIS rotation plays a more important role. In other words, we can obtain a considerable improvement by properly rotating the RIS rather than moving it over a wide area. For instance, we can achieve more than 200\% performance improvement through rotating the RIS by 42.14$^{\circ}$, while an 150\% improvement is obtained by shifting the RIS over 400 meters.