Hardware Efficient Massive MIMO Systems with Optimal Antenna Selection.

Massive MIMO is currently a leading physical layer technology candidate that can dramatically enhance throughput in 5G systems, for both unicast and multicast transmission modalities. As antenna elements are becoming smaller and cheaper in the mmW range compared to radio frequency (RF) chains, it is crucial to perform antenna selection at the transmitter, such that the available RF chains are switched to an appropriate subset of antennas. This paper considers the joint problem of multicast beamforming and antenna selection for a single multicast group in massive MIMO systems. The prior state-of-art for this problem relies on semi-definite relaxation (SDR), which cannot scale up to the massive MIMO regime. A successive convex approximation (SCA) based approach is proposed to tackle max-min fair joint multicast beamforming and antenna selection. The key idea of SCA is to successively approximate the non-convex problem by a class of non-smooth, convex optimization problems. Two fast and memory efficient first-order methods are proposed to solve each SCA subproblem. Simulations demonstrate that the proposed algorithms outperform the existing state-of-art approach in terms of solution quality and run time, in both traditional and especially in massive MIMO settings.

As an effective technology for boosting the performance of wireless communications, massive multiple-input multiple-output (MIMO) systems based on symmetric antenna arrays have been extensively studied. Using low-resolution analog-to-digital converters (ADCs) at the receiver can greatly reduce hardware costs and circuit complexity to further improve the energy efficiency (EE) of the system. There are significant research on the design of MIMO detectors but there is limited study on their performance in terms of EE. This paper studies the effect of signal detection on the EE in practical systems, and proposes to apply several signal detectors based on lattice reduction successive interference cancellation (LR-SIC) to massive MIMO systems with low-precision ADCs. We report results on their achievable EE in fading environments with typical modeling of the path loss and detailed analysis of the power consumption of the transceiver circuits. It is shown that the EE-optimal solution depends highly on the application scenarios, e.g., the number of antennas employed, the cell size, and the signal processing efficiency. Consequently, the signal detector must be properly selected according to the application scenario to maximize the system EE. In addition, medium-resolution ADCs should be selected to balance their own power consumption and the associated nonlinear distortion to maximize the EE of system.

Multiple Transmit Antenna Selection in MIMO-OSTBC System in Nakagami-m Fading Channel

Intelligent reflecting surface (IRS), which uses passive reflective elements instead of active radio frequency (RF) chains, is a cost and energy-efficient solution to improve the wireless system performance. With a similar objective, transmit antenna selection (AS) reduces the number of RF chains at the base station while harnessing the benefits of multiple antennas. In our work, we focus on joint optimization of antenna subset and transmit beamforming at the base station, and passive beamforming at the IRS to maximize the receive signal power. For single AS, we first derive a closed-form optimal rule. We then propose a simpler AS rule, which significantly reduces the computational complexity and the number of pilot transmissions required. For a system with Nt antennas at the base station and N IRS elements, the optimal AS rule requires N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> + N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> N pilots. However, the proposed simpler rule requires only 2N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> +N pilots. For subset AS, we develop a manifold optimization based algorithm. To reduce its subset search complexity, which is exponential in the number of RF chains at the base station, we propose an alternating optimization based iterative algorithm. Our numerical results show that the proposed simpler AS rules are near optimal.

This paper designs a novel hybrid (a mixture of analog and digital) beamforming and examines the relation between the hybrid and digital beamformings for downlink multiuser massive multiple input multiple output (MIMO) systems. We assume that perfect channel state information is available only at the transmitter and we consider the total sum rate maximization problem. For this problem, the hybrid beamforming is designed indirectly by considering a weighed sum mean square error (WSMSE) minimization problem incorporating the solution of digital beamforming which is obtained from the block diagonalization technique. The resulting WSMSE problem is solved by applying the theory of compressed sensing. The relation between the hybrid and digital beamformings is studied numerically by varying different parameters, such as the number of radio frequency (RF) chains, analog to digital converters (ADCs) and multiplexed symbols. Computer simulations reveal that for the given number of RF chains and ADCs, the performance gap between digital and hybrid beamformings can be decreased by decreasing the number of multiplexed symbols. Moreover, for the given number of multiplexed symbols, increasing the number of RF chains and ADCs will increase the total sum rate of the hybrid beamforming which is expected.

Recently massive MIMO systems have attracted a lot of attention. A serious limitation in implementing massive MIMO is the number of RF chains required, which is due to its high implementation cost and power consumption. Antenna selection is a wise remedy, that not only reduces the number of required RF chains, but also improves the system’s performance. In this work, two low complexity capacity-maximizing transmit antenna selection (TAS) schemes, one for low SNR region and one for medium to high SNR region, are proposed. It is shown that the proposed antenna selection schemes enhance the MIMO link’s capacity at low SNRs, and select only one antenna in more than $$90\%$$ of channel realizations, which in terms of numbers of required RF chains is very promising. The performance of the proposed schemes are compared to an existing high performance greedy TAS technique in terms of average capacity and complexity. Finally, a close analytical approximation for average capacity of the proposed capacity-based subset selection scheme is presented.

Precoding/combining and large antenna arrays are essential in millimeter wave (mmWave) systems. In traditional MIMO systems, precoding/combining is usually done digitally at baseband with one radio frequency (RF) chain and one analog-to-digital converter (ADC) per antenna. The high cost and power consumption of RF chains and ADCs at mmWave frequencies make an all-digital processing approach prohibitive. When only a limited number of RF chains is available, hybrid architectures that split the precoding/combining processing into the analog and digital domains are attractive. A previously proposed hybrid solution employs phase shifters and mixers in the RF precoding/combining stage. It obtains near optimal spectral efficiencies with a reduced number of RF channels. In this paper we propose a different hybrid architecture, which simplifies the hardware at the receiver by replacing the phase shifters with switches. We present a new approach for compressed sensing based channel estimation for the hybrid architectures. Given the channel estimate, we propose a novel algorithm that jointly designs the antenna subsets selected and the baseband combining. Using power consumption calculations and achievable rates, we compare the performance of hybrid combining with antenna switching and phase shifting, showing that antenna selection is preferred in a range of operating conditions.

Although massive multiple-input multiple-output (MIMO) can enhance the overall system performance significantly, it could suffer from high cost and power consumption issues due to using a large number of radio frequency (RF) chains. Two different approaches are commonly exploited to overcome these issues. The first approach is using hybrid beamforming, which consists of analog and digital beamforming, to reduce the total number of RF chains. The second approach is adopting low-resolution analog-todigital converters (ADCs) for each RF chain. For both approaches, channel estimation becomes a difficult task. This paper addresses the problem of channel angle of arrival (AoA) estimation in massive MIMO using both hybrid beamforming and one-bit magnitudeaided (OMA) ADCs. An iterative algorithm is developed to estimate the channel AoA, and the appropriate threshold per iteration is analyzed. Numerical results show that the proposed technique can achieve sufficient AoA estimation performance with practical values of the signal-to-noise ratio (SNR).

High power consumption and hardware cost are the two challenges for practical massive MIMO systems. One promising solution is to employ low resolution analog to digital converter (ADC). In this paper, massive MIMO relaying systems are investigated with double resolution ADCs at base station receiver, where some antennas are connected to low resolution ADCs, while the rest of the antennas are connected to high resolution ADCs. Closed form expression for uplink spectral efficiency (SE) is derived using the maximum ratio combining receiver assuming perfect channel state information. Some asymptotic analysis is discussed to explore the effects of system parameters on SE such as; the number of receive antennas, ADC quantization bits, user transmit power and the rate of high precision ADC in double resolution ADC structure. Moreover, energy efficiency with some system parameters is studied. It is shown that the performance wastage owing to low precision ADCs can be recompensed by growing the number of receive antennas $$ \varvec{N} $$, following a logarithmic scaling law instead of increasing the transmit power at both of source and relay. The double resolution ADCs can achieve a better tradeoff between power consumption and SE when using 4 quantization bits. By using only around 30 antennas, the double resolution ADCs achieves its optimal energy efficiency for selecting system parameters.

Massive Multiple-Input Multiple-Output (Massive MIMO) serves as a foundational enabling technology for 5G and future communication systems, markedly boosting spectral and energy efficiency through the deployment of large-scale antenna arrays. However, the scaling-up of antenna arrays has led to a substantial increase in system power consumption and hardware costs, with high-precision analog-to-digital converters (ADCs) emerging as the dominant power consumption bottleneck in the radio frequency chain. To alleviate system complexity and power consumption, low-resolution ADCs (13 bits) have attracted extensive research interest in recent years. Such schemes can substantially curtail hardware costs and energy consumption while retaining satisfactory system performance. Nevertheless, the introduction of severe nonlinear distortion due to low-precision quantization disrupts the linear Gaussian model assumption upon which traditional receiver algorithms rely, resulting in compromised channel estimation and signal detection performance. Quantization errors demonstrate non-Gaussian and input-dependent characteristics, leading to the degradation of amplitude information and thus constraining the applicability of technologies such as high-order modulation and high-precision sensing. This paper presents a systematic review of low-precision quantization techniques for Massive MIMO. It first investigates the impacts of low-bit quantization on system models and signal statistical properties. Subsequently, it elaborates on transceiver architectures and key design challenges pertaining to low-precision ADCs/DACs. The paper highlights signal processing and algorithmic strategies to overcome quantization distortion, including Bussgang decomposition linearization methods, statistical inference techniques such as approximate message passing (AMP), model-driven deep learning frameworks, and quantization architectures endowed with noise-shaping capabilities. Finally, it discusses the challenges and future directions of this technology in emerging scenarios, including terahertz communications, intelligent reflecting surfaces, and integrated sensing and communication. This paper seeks to provide researchers with a systematic technical overview, clarifying the intrinsic connections and trade-offs among different methods, and offering valuable insights for the realization of high-energy-efficiency and low-cost Massive MIMO systems.

This paper considers a cooperative wireless system with multiple antennas at the source. A multi-antenna relay equipped with a power splitter is introduced between the source and destination. It utilizes the energy from the received signal for amplification and transmit it to the destination. The linear precoding techniques such as zero forcing (ZF) and minimum mean square error (MMSE) are used to reduce the interference. We compare two antenna selection techniques viz. norm-based antenna selection (NBAS) and received signal-to-noise ratio (SNR) based antenna selection (RSBAS). It is analyzed that the NBAS and RSBAS antenna selection techniques offer improvement of 20% and 37% over random antenna selection technique. However, the antenna selection technique NBAS offers improvement in system bit error rate (BER) performance by 14% as compared to RSBAS at SNR value of 0 dB. We presented a scenario where the energy harvesting at relay node offers saving the battery power at the user node. Hence, the presented analysis is applicable in communication with remote sensors. This paper also analyses that the BER is improved with the reduction in the distance between source and relay for the given value of energy harvesting efficiency.

One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs. To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach. The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error. Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity. Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.

High-speed high resolution analog-to-digital converter (ADC) is the key design blocks in mixed-signal chip design since the ADC is an interface between digital signal processing systems and the analog world. With the rapid advancement of CMOS fabrication technology, more and more signal-processing functions are implemented in the digital domain for a lower cost, lower power consumption, higher yield, and higher re-configurability. This has recently generated a great demand for low-power ADCs that can be realized in a mainstream deep sub-micrometer CMOS technology. On the other hand, since analog circuits tend to be sensitive to fluctuations in temperature and the fabrication process, circuit designers are forced to allow for a wide margin to ensure the desired operation of all the chips under various conditions. Therefore, if the margins can be minimized, both reduction of power dissipation and a higher yield in mass production can be achieved. To tap onto the waves of the rapid booming wireless communication markets, high resolution and high speed A/D converter is required at very low power consumption. Providing the wanted resolution at the required speed for the minimum power consumption and silicon cost is what's driving the use of specific A/D architectures [1]. Architectures that have been applied include flash, subranging, successive approximation, sigma-delta and pipelined. Each of these architectures has their specific advantages and drawbacks. Pipelined architecture poised as a suitable candidate since the power consumption will be an order of magnitude better comparing to flash ADC, subranging ADC, successive approximation ADC and oversampling ADC. Moreover, a correction algorithm can be employed to compensate for the errors in the sub-ADC. Shooting for even higher speed, parallelism is required to be incorporated into pipelined structure. Hence, in this work, a feasibility study of a novel subranging pipelined A/D converter with 100 MS/s and 8-bit resolution is conducted. This research describes the design of a 8-bit 100 MS/s subranging pipelined analog-to-digital converter(ADC) suitable for wide-range applications. To achieve the wide-range performance, this work purposes a novel architectural design at both system and circuit levels. At the device level, device modeling and active components characterisations are carefully studied. Thin gate oxide, NFET is fabricated in Chartered 90nm CMOS process. The results showed that the models used in SPIC simulation correlate well with the measured results except at the end of high VDD where breakdown is seen in the measured results but not during simulation. At the circuit level, a 3-bit coarse ADC and a 6-bit fine ADC are developed. Logic designs are also carried out. At the system level, this work investigates the effects of ADC design parameters such as DNL and INL in the static DC aspects and SFDR and ENOB in the dynamic AC aspects. A top-down design methodology is adopted in this work. The behavioral modelling results verify the success of the proposed architecture. The simulation results showed that the designed ADC is able to achieve a 7.4 effective bits out of 8 bit resolution.

&lt;em&gt;&lt;span&gt;Multiple antenna configurations can be used to increase the data throughput reducing the effects of multipath fading and interference when channel bandwidth is limited. Orthogonal Space Time Block Codes along with Transmit antenna selection can improve the performance of multiple input multiple output systems. In this paper, we present the Transmit Antenna Selection (TAS) technique based on the Maximal Ratio Combining (MRC) scheme with single antenna selection for image transmission. The performance analysis of the system was carried out under different fading channels i.e. Rayleigh and Rician channel for image input. We design end to end TAS/MRC system in Simulink with advancements in the channel designs and receive diversity techniques along with the feedback models. The Bit Error Rate (BER) analysis was performed for the combinations of number of transmit and receive antennas for TAS/MRC system for various fading environments.&lt;/span&gt;&lt;/em&gt;

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