Performance of ZF precoder in downlink massive MIMO with non-uniform user distribution

We investigate the achievable sum rate and energy efficiency of zero-forcing precoded downlink massive multipleinput multiple-output systems in Ricean fading channels. A simple and accurate approximation of the average sum rate is presented, which is valid for a system with arbitrary rank channel means. Based on this expression, the optimal power allocation strategy maximizing the average sum rate is derived. Moreover, considering a general power consumption model, the energy efficiency of the system with rank-1 channel means is characterized. Specifically, the impact of key system parameters, such as the number of users N, the number of BS antennas M, Ricean factor K and the signal-to-noise ratio (SNR) ρ are studied, and closed-form expressions for the optimal ρ and M maximizing the energy efficiency are derived. Our findings show that the optimal power allocation scheme follows the water filling principle, and it can substantially enhance the average sum rate in the presence of strong line-of-sight effect in the low SNR regime. In addition, we demonstrate that the Ricean factor K has significant impact on the optimal values of M, N and ρ.

In this paper, we investigate the coexistence of two technologies that have been put forward for the fifth generation (5G) of cellular networks, namely, network-assisted device-to-device (D2D) communications and massive MIMO (multiple-input multiple-output). Potential benefits of both technologies are known individually, but the tradeoffs resulting from their coexistence have not been adequately addressed. To this end, we assume that D2D users reuse the downlink resources of cellular networks in an underlay fashion. In addition, multiple antennas at the BS are used in order to obtain precoding gains and simultaneously support multiple cellular users using multiuser or massive MIMO technique. Two metrics are considered, namely the average sum rate (ASR) and energy efficiency (EE). We derive tractable and directly computable expressions and study the tradeoffs between the ASR and EE as functions of the number of BS antennas, the number of cellular users and the density of D2D users within a given coverage area. Our results show that both the ASR and EE behave differently in scenarios with low and high density of D2D users, and that coexistence of underlay D2D communications and massive MIMO is mainly beneficial in low densities of D2D users.

Full-duplex (FD) transmission holds a great potential of improving the sum data rate of wireless communication systems. However, the self-interference introduced by the full-duplex transmitter brings a big challenge to enhance the energy efficiency. This paper investigates the power allocation problem in a full-duplex two-way (FDTW) communication network over an OFDM channel, aiming at improving the sum data rate and energy efficiency. We first characterize the sum rate and energy efficiency achieved in a single-carrier FDTW system. The optimal transmit power that achieves the maximal sum data rate is presented. The energy efficiency maximization problem is solved by using fractional programming. Then we further formulate sum rate and energy efficiency maximization problem in a multi-subcarrier FDTW system. In particular, the sub-optimal transmit power allocation which achieves a decent sum rate improvement is found by using a proposed iterative algorithm. By combining the iterative algorithm and fractional programming, we further maximize the energy efficiency of the multi-subcarrier system. With our proposed algorithm, we can easily obtain an optimal transmit power that approximates the global optimal solution. Simulation results show that using the obtained optimal transmit power allocation algorithm can significantly improve the sum rate and energy efficiency in both single-carrier and multi-subcarrier systems.

To meet up with the ever increasing subscribers' demand for higher data rates and mobile data traffic growth in the telecommunication industry, the fifth generation (5G) systems is being considered for the next future cellular communication standards. The two principal design requirements being aimed at in 5G are robust data transmission rates in Gigabits and low power consumption systems. Massive multiple input multiple output (M-MIMO) technology is an evolving smart antenna technology which has some key promising potentials to boost 5G networks in meeting the aforementioned requirements. However, there is an emergent concern that increased number of antenna arrays in M-MIMO system could induce high power consumption and poor energy efficiency when deployed at the base stations (BSs). Also, inter-cellular interference which occurs as a result of pilot contamination, fast fading and uncorrelated noise effects in the radio channels are other open issues in M-MIMO system. This work investigates and compare the achievable sum rates and energy efficiency of a downlink single cell M-MIMO systems utilizing linear and nonlinear precoding schemes. First, we have shown how the increasing signal-to-noise ratio and M-antennas impact the achievable sum rates. Furthermore, the energy saving potentials of M-MIMO systems in macro, micro and pico cellular environments when linear and nonlinear precoding schemes are utilized at the BS have been demonstrated. Particularly, by means of power fairness index, the tradeoff among the energy efficiency, sum rate and the system users have also been presented and discussed. Results show that substantial energy efficiency improvements can be achieved in micro and pico cellular environments of downlink M-MIMO systems when non-linear successive interference cancellation precoding is applied compared to linear precoding schemes.

User distribution in ultradense networks (UDNs) plays a crucial role in affecting the performance of UDNs due to the essential coupling between the traffic and the service provided by the networks. Existing studies are mostly based on the assumption that users are uniformly distributed in space. The nonuniform user distribution has not been widely considered despite that it is much closer to the real scenario. In this paper, radiation and absorbing model (R&A model) is first adopted to analyze the impact of the nonuniformly distributed users on the performance of 5G UDNs. Based on the R&A model and queueing network theory, the stationary user density in each hot area is investigated. Furthermore, the coverage probability, network throughput, and energy efficiency are derived based on the proposed theoretical model. Compared with the uniformly distributed assumption, it is shown that nonuniform user distribution has a significant impact on the performance of UDNs.

Green wireless communication has drawn ample interest in the 5G cellular networks. In this article, we investigate an energy‐efficient power allocation with spectrum sensing for cognitive radio (CR) networks. The optimization problem is formulated as a ratio of the sum rate (SR) to the total power consumption in the signal transmission and spectrum sensing subject to the total power constraint. Because the original optimization problem is nonconvex, we adopt Dinkelback's method and propose an optimal energy‐efficient power allocation scheme that iteratively improves the energy efficiency (EE) performance of the CR network and finally gives an optimal solution. In addition, an expression for the optimal solution is derived through the dual‐decomposition method, and its performance is validated through simulation results. The impact of the probability of detection (PD) and the probability of PU activeness (PA) on the achievable average EE and SR is demonstrated when the CR network is designed from the energy‐efficient perspective. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Coordinated multi-point (CoMP) transmission is considered as an efficient technique to improve cell-edge performance as well as system spectrum efficiency. In CoMP-enabled systems, a cluster of coordinated base stations (BSs) are typically assumed to be connected to a control unit (CU) via backhaul links, and the provided performance gain relies heavily on the quality of the channel state information (CSI) available at the CU side. In this paper, we consider the downlink of a CoMP cluster and compare three different CoMP transmission schemes: zero-forcing coherent joint transmission, non-coherent joint transmission and coordinated scheduling. Moreover, for each of the analyzed schemes, the performance in terms of average sum rate of the CoMP cluster is studied with predicted CSI, considering the effects of the feedback and backhaul latency, as well as the user mobility. Compared to zero-forcing coherent joint transmission, we show that non-coherent joint transmission and coordinated scheduling are more robust to channel uncertainty. In addition, depending on the latency, user mobility and user locations, different schemes would achieve the highest average sum rate performance. Hence, a system could switch between the transmission schemes to improve the sum rate.

In this paper, the average sum rate of two-way amplify-and-forward (AF) half-duplex relaying system is analyzed. To this end, we first derive the harmonic mean of two independent gamma distributed random variables which have the same shape parameter but different scale parameters. By deriving tight upper and lower bounds for the average sum rate of two-way relaying, we verify that two-way relaying can significantly recover the spectrum efficiency loss of one-way relaying. We also extend the two-way AF half-duplex relaying to the case where source and destination terminals both transmit Alamouti's orthogonal space time block code (OSTBC) utilizing two antennas and the relay has only one antenna. By deriving both upper and lower bounds for the average sum rate as well as an upper bound for the pairwise error probability (PEP) for the proposed two-way OSTBC scheme, we show that the average sum rate is further improved compared to the single antenna case and a diversity order of two is also achieved. Furthermore, optimal power allocations under a global power constraint for two-way relaying with single antenna and the proposed two-way OSTBC scheme are derived analytically.

Quantum aided efficient resource control for connected support in IRS assisted networks

In this paper, a downlink single-cell non-orthogonal multiple access (NOMA) network with uniformly deployed users is considered and an analytical framework to evaluate its performance is developed. Particularly, the performance of NOMA is studied by assuming two types of partial channel state information (CSI). For the first one, which is based on imperfect CSI , we present a simple closed-form approximation for the outage probability and the average sum rate, as well as their high signal-to-noise ratio (SNR) expressions. For the second type of CSI, which is based on second order statistics (SOS) , we derive a closed-form expression for the outage probability and an approximate expression for the average sum rate for the special case two users. For the addressed scenario with the two types of partial CSI, the results demonstrate that NOMA can achieve superior performance compared to the traditional orthogonal multiple access (OMA). Moreover, SOS-based NOMA always achieves better performance than that with imperfect CSI, while it can achieve similar performance to the NOMA with perfect CSI at the low SNR region. The provided numerical results confirm that the derived expressions for the outage probability and the average sum rate match well with the Monte Carlo simulations.

In this paper, we study efficient codebook design for limited feedback in extremely large-scale multiple-input-multiple-output (XL-MIMO) frequency division duplexing (FDD) systems. It is worth noting that existing codebook designs for XL-MIMO, such as the polar-domain codebook, have not well taken into account user (location) distribution in practice, thereby incurring excessive feedback overhead. To address this issue, we propose in this paper a novel and efficient feedback codebook tailored to the user distribution. To this end, we first consider a typical scenario where users are uniformly distributed within a specific polar-region, based on which a sum-rate maximization problem is formulated to jointly optimize angle-range samples and bit allocation among angle/range feedback. This problem is challenging to solve due to the lack of a closed-form expression for the received power in terms of angle and range samples. By leveraging a Voronoi partitioning approach, we show that uniform angle sampling is optimal for received power maximization. For the more challenging range sampling design, we obtain a tight lower bound on the received power and show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">geometric</i> sampling, where the ratio between adjacent samples is constant, can maximize the lower bound and thus serves as a high-quality suboptimal solution. We then extend the proposed framework to accommodate more general non-uniform user distribution via an alternating sampling method. Furthermore, theoretical analysis reveals that as the array size increases, the optimal allocation of feedback bits increasingly favors range samples at the expense of angle samples. Finally, numerical results validate the superior rate performance and robustness of the proposed codebook design under various system setups, achieving significant gains over benchmark schemes, including the widely used polar-domain codebook.

Near-orthogonal transmission over MIMO broadcast channels with limited feedback is addressed, identifying the intrinsic tradeoffs that govern the system performance in terms of sum rate. A low-complexity scheme is proposed for joint scheduling and beamforming, which employs feedback in the form of quantized channel directions and lower bounds on each user's received SINR, given a maximum orthogonality factor epsi between transmit beamforming vectors (epsi = 0 if orthogonal beamforming). The authors assume a simple power allocation, which consists of distributing the available power equally over the active beams. In a system with K active users and a given average SNR, the authors show that the sum rate is a function of the number of antennas, the number of active beams (considered less than or equal to the number of antennas), the orthogonality factor epsi and the quantization codebook size. A practical rate function is derived for the described system which approximates the average sum rate accurately as validated through computer simulations. Based on the proposed rate function, optimality of SDMA vs. TDMA systems with asymptotically large number of users is shown. In addition, the authors provide a performance comparison between the proposed algorithm with limited feedback and other approaches with different level of channel state information at the transmitter.

The exponential growth of data traffic in 6G drives the dense deployment of small cells. While spectrum reuse enhances capacity, it inevitably exacerbates inter-user and inter-cell interference. Consequently, power allocation is vital for maximizing the instantaneous downlink sum rate subject to transmit power constraints. However, traditional optimization methods, such as fractional programming, rely on global Channel State Information (CSI) and incur high computational complexity, rendering them impractical for dynamic, large scale networks. Although Deep Reinforcement Learning (DRL) offers a promising alternative, existing methods are limited by full CSI dependence, misalignment with instantaneous objectives, and inefficient exploration, hindering robustness in dynamic environments. To address these challenges, this study proposes an enhanced DRL-based power allocation framework termed Information-Gain Deep Deterministic Policy Gradient (IG-DDPG). First, a compact state representation is designed by retaining only the top-I strongest normalized interference channel gains relative to the serving link, while discarding weak and negligible interference sources. This selection rationale effectively preserves dominant interference information to maintain decision accuracy while significantly reducing the state space dimensionality to remove the dependency on global CSI. Second, a max-reward learning mechanism is incorporated into the Deep Deterministic Policy Gradient (DDPG) architecture to directly optimize instantaneous sum rate. Third, building upon the Intrinsic Curiosity Module (ICM), we reinterpret its prediction-error signal as an information-gain surrogate and integrate it as an intrinsic objective. A weighting parameter is introduced to balance the trade-off between the extrinsic instantaneous rate and the intrinsic information gain, guiding the agent toward dynamically informative state transitions. Extensive experiments demonstrate that IG-DDPG consistently outperforms the baseline DDPG algorithm. It improves the average sum rate by no less than 4.3%, increases the top 20% average sum rate by at least 2.4%, and reduces variance by up to 22.15%. These results confirm that IG-DDPG provides a scalable and robust solution for real-time interference management in 6G ultra-dense cellular networks.

Rate Splitting (RS) is a versatile and powerful technique for multi-antenna transmission. In this paper, we study the precoding optimization for RS, which is critically important for improving the system performance but often challenging to address. We first investigate the non-convex sum rate maximization problem under perfect Channel State Information at the Transmitter (CSIT). By constructing a separable structure for the sum-of-functions-of-ratios problem and jointly leveraging the Convex Concave Procedure (CCP) and the Alternating Direction Method of Multipliers (ADMM), we obtain a fast algorithm that substantially reduces the overall computational time through the parallel computation and explicit closed-form solutions. We then investigate the average sum rate maximization problem under imperfect CSIT, which is known as a more challenging non-convex stochastic problem. To obtain a fast algorithm for practical use, we carefully approximate the non-convex stochastic problem to a non-convex deterministic one with acceptable performance loss and tailor the fast algorithm derived from perfect CSIT for imperfect CSIT with modest changes. Numerical results show that compared to the state-of-the-art algorithms, the proposed algorithms achieve comparable sum rates or average sum rates but short computation times for large problem sizes, owing to the unique parallel computation structures and few matrix inverse operations.

User distribution is a critical issue in cellular networks. In most previous works, a uniform user distribution is often assumed within a cell. In this paper, we purpose a non-uniform user distribution model. Both its effects on downlink and uplink capacities of a cellular network are evaluated under two common packet scheduling algorithms: Maximum Carrier to Interference scheduling and Round Robin scheduling. Simulation results show that when user distribution center coincides with cell center, the higher the user concentration, the larger the system capacity. And when user distribution center deviates from cell center, the loss of system capacity is much greater at higher user concentration. KeywordsUser Distribution; Max-C/I Scheduling; Round Robin Scheduling; Cellular Networks