In this paper, we propose an efficient regularized zero-forcing (RZF) precoding method that has lower hardware resource requirements and produces a shorter delay to the first transmitted symbol compared with truncated polynomial expansion (TPE) that is based on Neumann series in massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme, named matrix decomposition-polynomial expansion (MDPE), essentially applies a matrix decomposition algorithm based on polynomial expansion to significantly reduce full matrix multiplication computational complexity. Accordingly, it is suitable for real-time hardware implementations and high-mobility scenarios. Furthermore, the proposed method provides a simple expression that links the optimization coefficients to the ratio of BS/UTs antennas (β). This approach can speed-up the convergence to the matrix inverse by a matrix polynomial with small terms and further reduce computation costs. Simulation results show that the MDPE scheme can rapidly approximate the performance of the full precision RZF and optimal TPE algorithm, while adaptively selecting matrix polynomial terms in accordance with the different β and SNR situations. It thereby obtains a high average achievable rate of the UTs under power allocation.

The transmission on fronthaul links in the cloud radio access network has become a bottleneck with the increasing data rate. In this paper, we propose a novel two-stage compression scheme for fronthaul links. In the first stage, the commonly used techniques like cyclic prefix stripping and sampling rate adaptation are implemented. In the second stage, a structure called linear prediction coding with decision threshold (LPC-DT) is proposed to remove the redundancies of signal. Considering that the linear prediction outputs have large dynamic range, a two-piecewise quantization with optimized decision threshold is applied to enhance the quantization performance. In order to further lower the transmission rate, a multi-level successive structure of lossless polar source coding is proposed to compress the quantization output with low encoding and decoding complexity. Simulation results demonstrate that the proposed scheme with LPC-DT and LPSC offers not only significantly better compression ratios but also more flexibility in bandwidth settings compared with traditional ones.

This paper studies the multi-link multi-antenna amplify-and-forward (AF) relay system, in which multiple source-destination pairs communicate with the aid of an energy harvesting (EH)-enabled relay and the relay utilizes the power splitting (PS) protocol to accomplish simultaneous EH and information forwarding (IF). Specifically, independent PS, i.e., allow each antenna to have an individual PS factor, and cooperative power allocation (PA) i.e., adaptively allocate the harvested energy to each channel, are proposed to increase the signal processing degrees of freedom and energy utilization. Our objective is to maximize the minimum rate of all source-destination pairs, i.e., the max-min rate, by jointly optimizing the PS and PA strategies. The optimization problem is first established for the ideal channel state information (CSI) model. To solve the formulated non-convex problem, the optimal forwarding matrix is derived and an auxiliary variable is introduced to remove the coupling of transmission rates in two slots, following which a bi-level iteration algorithm is proposed to determine the optimal PS and PA strategy by jointly utilizing the bisection and golden section methods. The proposal is then extended into the partial CSI model, and the final transmission rate for each source-destination pair is modified by treating the CSI error as random noise. With a similar analysis, it is proved that the proposed bi-level algorithm can also solve the joint PS and PA optimization problem in the partial CSI model. Simulation results show that the proposed algorithm works well in both ideal CSI and partial CSI models, and by means of independent PS and cooperative PA, the achieved max-min rate is greatly improved over existing non-EH-enabled and EH-enabled relay schemes, especially when the signal processing noise at the relay is large and the sources use quite different transmit powers.

In this paper, resource-efficient multiple description coding (MDC) multicast is investigated in cognitive radio networks with the consideration of imperfect spectrum sensing and imperfect channel feedback. Our objective is to maximize the system goodput, which is defined as the total successfully received data rate of all multicast users, while guaranteeing the maximum transmit power budget and the maximum average received interference constraint. Owing to the uncertainty of the spectrum state and the non-closed-form expression of the objective function, it is difficult to solve the problem directly. To circumvent this problem, a pretreatment is performed, in which we first estimate the real spectrum state of primary users and then propose a Gaussian approximation for the probability density functions of transmission channel gains to simplify the computation of the objective function. Thereafter, a two-stage resource allocation algorithm is presented to accomplish the subcarrier assignment, the optimal transmit channel gain to interference plus noise ratio (T-CINR) setting, and the transmit power allocation separately. Simulation results show that the proposed scheme is able to offset more than 80% of the performance loss caused by imperfect channel feedback when the feedback error is not high, while keeping the average interference on primary users below the prescribed threshold.

This paper studies the energy-saving problem in cognitive multicast orthogonal frequency-division multiplexing (OFDM) systems, for which a time-frequency two-dimensional model is established to enable the system energy conservation through joint temporal and spectral adaptations. The formulated two-dimensional problem, minimizing the total power consumption whilst guaranteeing the minimal-rate requirement for each multicast session and constraining the maximal perceived interference in each timeslot for the active primary user, is categorized as mixed integer non-convex programming, whose optimal solution is intractable in general. However, based on the time-sharing property, an asymptotically optimal algorithm is proposed by jointly iterating spectrum element (SE) assignment and power allocation. Moreover, a suboptimal algorithm, which carries out SE assignment and power allocation sequentially, is presented as well to reduce the computation complexity. Simulation results show the proposed joint algorithm can achieve the near-optimal solution, and the proposed sequential algorithm approximates to the joint one very well with a gap of less than 3%. Compared with the existing slot-by-slot energy-saving algorithms, the total power consumption is considerably decreased due to the combined exploitation of time and frequency dimensions.

Cognitive orthogonal frequency-division multiplexing (OFDM) systems are spectrum-efficient yet vulnerable to intercarrier interference (ICI), especially in high-mobility scenarios. In this paper, the energy efficiency optimization problem in high-mobility cognitive OFDM system is considered. The aim is to maximize the energy efficiency by adapting subcarrier bandwidth, power allocation and sensing duration in the presence of ICI, under the constraints of the total power budget of secondary networks, the probabilistic interference limits for the protection of primary networks, and the subcarrier spacing restriction for high-mobility OFDM systems. In order to tackle the intractable non-convex optimization problem induced by ICI, an ICI-aware power allocation algorithm is proposed, by referring to noncooperative game theory. Moreover, a near-optimal subcarrier bandwidth search algorithm based on golden section methods is also presented to maximize the system energy efficiency. Simulation results show that the proposed algorithms can achieve a considerable energy efficiency improvement by up to 133% compared to the traditional static subcarrier bandwidth and power allocation schemes.